if (!require("dplyr")) install.packages("dplyr")
## Loading required package: dplyr
##
## Attaching package: 'dplyr'
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##
## filter, lag
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##
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if (!require("skimr")) install.packages("skimr")
## Loading required package: skimr
if (!require("tidyr")) install.packages("tidyr")
## Loading required package: tidyr
if (!require("survival")) install.packages("survival")
## Loading required package: survival
if (!require("survminer")) install.packages("survminer")
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##
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if (!require("haven")) install.packages("haven")
## Loading required package: haven
if (!require("broom")) install.packages("broom")
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if (!require("rms")) install.packages("rms")
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##
## Attaching package: 'Hmisc'
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if (!require("tidyverse")) install.packages("tidyverse")
## Loading required package: tidyverse
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## ✔ forcats 1.0.0 ✔ readr 2.1.5
## ✔ lubridate 1.9.4 ✔ stringr 1.5.1
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if (!require("tableone")) install.packages("tableone")
## Loading required package: tableone
library(dplyr)
library(skimr)
library(tidyr)
library(survival)
library(survminer)
library(haven)
library(broom)
library(rms)
library(tidyverse)
library(tableone)
NHANES2 <- read.csv("NHANES2-1 (1).csv")
d <- NHANES2 #%>%
#select('ROWNAMES','SEX','RACE','MARRY','DEATH','AGEYRS',
#'GRADES','WT', 'BOOZE', 'SIZE',
#'AVGSMK', "HEIGHT", "EXAM_YR", "DIE_YR", "LAST_YR")
#Exclude missing death
d <- d %>%
filter(!is.na(BOOZE), !is.na(DEATH), !is.na(SEX), !is.na(RACE), !is.na(GRADES), !is.na(AVGSMK), !is.na(SIZE), !is.na(GRADES))
#BMI
d <- d %>%
mutate(BMI = WT / (HEIGHT / 100)^2)
head(d$BMI)
## [1] 20.49522 21.02151 23.22748 35.72785 27.92312 30.50132
# GRADES and SIZE categories
d$EDUC_CAT <- cut(d$GRADES,
breaks = c(-Inf, 8, 11, 12, 15, Inf),
labels = c("≤8 yrs", "Some HS", "HS Grad", "Some College", "College+"),
right = TRUE)
d$SIZE_CAT <- cut(d$SIZE,
breaks = c(0, 3, 5, 7, 8),
labels = c("Rural", "Small town", "Medium city", "Large city"),
right = TRUE)
# Catergorical BOOZE
d <- d %>%
mutate(BOOZE_q = cut(
BOOZE,
breaks = c(-1, 0, 0.5, 2.0, 77.0),
include.lowest = TRUE,
labels = c("0/week", "0–0.5/week", "0.5–2/week", ">2/week")
))
vars <- c("AGEYRS", "SEX", "RACE", "MARRY", "BMI", "AVGSMK", "EDUC_CAT", "SIZE_CAT")
catVars <- c("SEX", "RACE", "MARRY")
#Table 1
table1 <- CreateTableOne(vars = vars,
data = d,
strata = "BOOZE_q",
factorVars = catVars)
print(table1, showAllLevels = TRUE)
## Stratified by BOOZE_q
## level 0/week 0–0.5/week 0.5–2/week
## n 4053 941 1729
## AGEYRS (mean (SD)) 57.09 (12.79) 54.34 (13.36) 51.60 (13.53)
## SEX (%) 1 1448 (35.7) 367 (39.0) 856 (49.5)
## 2 2605 (64.3) 574 (61.0) 873 (50.5)
## RACE (%) 1 3497 (86.3) 827 (87.9) 1515 (87.6)
## 2 475 (11.7) 93 ( 9.9) 194 (11.2)
## 3 81 ( 2.0) 21 ( 2.2) 20 ( 1.2)
## MARRY (%) 2 2885 (71.2) 683 (72.6) 1288 (74.5)
## 3 671 (16.6) 127 (13.5) 172 ( 9.9)
## 4 190 ( 4.7) 67 ( 7.1) 102 ( 5.9)
## 5 96 ( 2.4) 20 ( 2.1) 59 ( 3.4)
## 6 202 ( 5.0) 40 ( 4.3) 103 ( 6.0)
## 8 9 ( 0.2) 4 ( 0.4) 5 ( 0.3)
## BMI (mean (SD)) 26.55 (5.50) 26.42 (5.10) 26.03 (4.84)
## AVGSMK (mean (SD)) 4.82 (10.84) 6.72 (12.63) 8.26 (13.77)
## EDUC_CAT (%) ≤8 yrs 1452 (35.8) 217 (23.1) 337 (19.5)
## Some HS 753 (18.6) 185 (19.7) 282 (16.3)
## HS Grad 1209 (29.8) 311 (33.0) 636 (36.8)
## Some College 353 ( 8.7) 130 (13.8) 235 (13.6)
## College+ 286 ( 7.1) 98 (10.4) 239 (13.8)
## SIZE_CAT (%) Rural 1101 (27.2) 348 (37.0) 758 (43.8)
## Small town 454 (11.2) 123 (13.1) 244 (14.1)
## Medium city 569 (14.0) 118 (12.5) 205 (11.9)
## Large city 1929 (47.6) 352 (37.4) 522 (30.2)
## Stratified by BOOZE_q
## >2/week p test
## n 2527
## AGEYRS (mean (SD)) 51.71 (13.18) <0.001
## SEX (%) 1678 (66.4) <0.001
## 849 (33.6)
## RACE (%) 2250 (89.0) 0.010
## 236 ( 9.3)
## 41 ( 1.6)
## MARRY (%) 1972 (78.0) <0.001
## 170 ( 6.7)
## 168 ( 6.6)
## 70 ( 2.8)
## 140 ( 5.5)
## 7 ( 0.3)
## BMI (mean (SD)) 25.20 (4.08) <0.001
## AVGSMK (mean (SD)) 9.60 (13.92) <0.001
## EDUC_CAT (%) 387 (15.3) <0.001
## 354 (14.0)
## 876 (34.7)
## 411 (16.3)
## 499 (19.7)
## SIZE_CAT (%) 1239 (49.0) <0.001
## 346 (13.7)
## 261 (10.3)
## 681 (26.9)
#Create follow-up time
d$start <- d$EXAM_YR + d$EXAM_MO / 12
d$end <- ifelse(d$DEATH == 1,
d$DIE_YR + d$DIE_MO / 12,
d$LAST_YR + d$LAST_MO / 12)
d$FU <- d$end - d$start
#Check for nonlinearity
##Spline Analysis
cox_nl <- coxph(Surv(FU, DEATH) ~ pspline(BOOZE, df = 4), data = d, ties = 'efron')
termplot(cox_nl, term = 1, se = TRUE,
xlab = "BOOZE (drinks/week)",
ylab = "Partial log hazard",
main = "Nonlinearity Check: BOOZE")
cox_nl1 <- coxph(Surv(FU, DEATH) ~ pspline(BOOZE, df = 4) + SEX + AGEYRS +
as.factor(RACE) + as.factor(EDUC_CAT) + as.factor(MARRY) + BMI +
AVGSMK + as.factor(SIZE_CAT), data = d, ties = 'efron')
summary(cox_nl1)
## Call:
## coxph(formula = Surv(FU, DEATH) ~ pspline(BOOZE, df = 4) + SEX +
## AGEYRS + as.factor(RACE) + as.factor(EDUC_CAT) + as.factor(MARRY) +
## BMI + AVGSMK + as.factor(SIZE_CAT), data = d, ties = "efron")
##
## n= 9250, number of events= 2145
##
## coef se(coef) se2 Chisq DF p
## pspline(BOOZE, df = 4), l 0.011611 0.004829 0.004455 5.78 1.00 1.6e-02
## pspline(BOOZE, df = 4), n 6.32 3.05 1.0e-01
## SEX -0.616923 0.049658 0.049598 154.34 1.00 1.9e-35
## AGEYRS 0.095053 0.002773 0.002770 1174.98 1.00 1.7e-257
## as.factor(RACE)2 -0.039245 0.075292 0.075262 0.27 1.00 6.0e-01
## as.factor(RACE)3 -0.323827 0.199131 0.199112 2.64 1.00 1.0e-01
## as.factor(EDUC_CAT)Some H -0.003546 0.062232 0.062225 0.00 1.00 9.5e-01
## as.factor(EDUC_CAT)HS Gra -0.068766 0.057582 0.057555 1.43 1.00 2.3e-01
## as.factor(EDUC_CAT)Some C -0.210260 0.082674 0.082642 6.47 1.00 1.1e-02
## as.factor(EDUC_CAT)Colleg -0.319397 0.088353 0.088311 13.07 1.00 3.0e-04
## as.factor(MARRY)3 0.093424 0.062865 0.062848 2.21 1.00 1.4e-01
## as.factor(MARRY)4 0.161688 0.101983 0.101975 2.51 1.00 1.1e-01
## as.factor(MARRY)5 0.286372 0.145131 0.145113 3.89 1.00 4.8e-02
## as.factor(MARRY)6 0.209858 0.096878 0.096874 4.69 1.00 3.0e-02
## as.factor(MARRY)8 0.251851 0.336471 0.336462 0.56 1.00 4.5e-01
## BMI -0.009028 0.004790 0.004790 3.55 1.00 5.9e-02
## AVGSMK 0.021489 0.001522 0.001522 199.26 1.00 3.0e-45
## as.factor(SIZE_CAT)Small 0.046129 0.069457 0.069446 0.44 1.00 5.1e-01
## as.factor(SIZE_CAT)Medium 0.028528 0.072274 0.072246 0.16 1.00 6.9e-01
## as.factor(SIZE_CAT)Large -0.007528 0.053610 0.053523 0.02 1.00 8.9e-01
##
## exp(coef) exp(-coef) lower .95 upper .95
## ps(BOOZE)3 0.6588 1.5179 0.33992 1.2769
## ps(BOOZE)4 0.5764 1.7349 0.31524 1.0539
## ps(BOOZE)5 0.6978 1.4331 0.38447 1.2665
## ps(BOOZE)6 0.7489 1.3352 0.39017 1.4376
## ps(BOOZE)7 0.9338 1.0709 0.45167 1.9307
## ps(BOOZE)8 1.1765 0.8500 0.52201 2.6514
## ps(BOOZE)9 1.3839 0.7226 0.56737 3.3755
## ps(BOOZE)10 1.6840 0.5938 0.59440 4.7707
## ps(BOOZE)11 2.0786 0.4811 0.51955 8.3159
## ps(BOOZE)12 2.4189 0.4134 0.34474 16.9723
## ps(BOOZE)13 2.6628 0.3755 0.17083 41.5058
## ps(BOOZE)14 2.9040 0.3443 0.06255 134.8187
## SEX 0.5396 1.8532 0.48956 0.5948
## AGEYRS 1.0997 0.9093 1.09376 1.1057
## as.factor(RACE)2 0.9615 1.0400 0.82960 1.1144
## as.factor(RACE)3 0.7234 1.3824 0.48962 1.0687
## as.factor(EDUC_CAT)Some H 0.9965 1.0036 0.88204 1.1257
## as.factor(EDUC_CAT)HS Gra 0.9335 1.0712 0.83391 1.0451
## as.factor(EDUC_CAT)Some C 0.8104 1.2340 0.68915 0.9529
## as.factor(EDUC_CAT)Colleg 0.7266 1.3763 0.61106 0.8640
## as.factor(MARRY)3 1.0979 0.9108 0.97065 1.2419
## as.factor(MARRY)4 1.1755 0.8507 0.96252 1.4356
## as.factor(MARRY)5 1.3316 0.7510 1.00192 1.7697
## as.factor(MARRY)6 1.2335 0.8107 1.02018 1.4914
## as.factor(MARRY)8 1.2864 0.7774 0.66523 2.4876
## BMI 0.9910 1.0091 0.98175 1.0004
## AVGSMK 1.0217 0.9787 1.01868 1.0248
## as.factor(SIZE_CAT)Small 1.0472 0.9549 0.91393 1.1999
## as.factor(SIZE_CAT)Medium 1.0289 0.9719 0.89304 1.1855
## as.factor(SIZE_CAT)Large 0.9925 1.0076 0.89351 1.1025
##
## Iterations: 5 outer, 13 Newton-Raphson
## Theta= 0.8419894
## Degrees of freedom for terms= 4 1 1 2 4 5 1 1 3
## Concordance= 0.78 (se = 0.005 )
## Likelihood ratio test= 2230 on 22.04 df, p=<2e-16
termplot(cox_nl1, term = 2, se = TRUE,
xlab = "BOOZE (drinks/week)",
ylab = "Partial log hazard",
main = "Nonlinearity Check: BOOZE")
##Higher Order
d <- d %>%
mutate(booze_2 = BOOZE^2,
booze_3 = BOOZE^3)
cox_lin <- coxph(Surv(FU, DEATH) ~ BOOZE + SEX + AGEYRS +
as.factor(RACE) + as.factor(EDUC_CAT) + as.factor(MARRY) + BMI +
AVGSMK + as.factor(SIZE_CAT), data = d, ties = 'efron')
cox.zph(cox_lin)
## chisq df p
## BOOZE 6.662 1 0.0098
## SEX 5.272 1 0.0217
## AGEYRS 2.190 1 0.1389
## as.factor(RACE) 2.306 2 0.3156
## as.factor(EDUC_CAT) 10.650 4 0.0308
## as.factor(MARRY) 2.794 5 0.7317
## BMI 0.875 1 0.3496
## AVGSMK 1.045 1 0.3067
## as.factor(SIZE_CAT) 4.626 3 0.2014
## GLOBAL 32.417 19 0.0280
plot(cox.zph(cox_lin))
### Model with BOOZE squared
model_quad <- coxph(Surv(FU, DEATH) ~ BOOZE + booze_2 + SEX + AGEYRS +
as.factor(RACE) + as.factor(EDUC_CAT) + as.factor(MARRY) + BMI +
AVGSMK + as.factor(SIZE_CAT), data = d, ties = 'efron')
summary(model_quad)
## Call:
## coxph(formula = Surv(FU, DEATH) ~ BOOZE + booze_2 + SEX + AGEYRS +
## as.factor(RACE) + as.factor(EDUC_CAT) + as.factor(MARRY) +
## BMI + AVGSMK + as.factor(SIZE_CAT), data = d, ties = "efron")
##
## n= 9250, number of events= 2145
##
## coef exp(coef) se(coef) z
## BOOZE -0.0113101 0.9887536 0.0075300 -1.502
## booze_2 0.0005518 1.0005520 0.0001923 2.869
## SEX -0.6104948 0.5430821 0.0495032 -12.332
## AGEYRS 0.0954759 1.1001824 0.0027669 34.507
## as.factor(RACE)2 -0.0350201 0.9655860 0.0752562 -0.465
## as.factor(RACE)3 -0.3200731 0.7260959 0.1990934 -1.608
## as.factor(EDUC_CAT)Some HS -0.0028686 0.9971355 0.0622085 -0.046
## as.factor(EDUC_CAT)HS Grad -0.0741294 0.9285515 0.0575052 -1.289
## as.factor(EDUC_CAT)Some College -0.2184510 0.8037629 0.0825254 -2.647
## as.factor(EDUC_CAT)College+ -0.3288082 0.7197811 0.0881777 -3.729
## as.factor(MARRY)3 0.0946112 1.0992314 0.0628430 1.506
## as.factor(MARRY)4 0.1643223 1.1785941 0.1019554 1.612
## as.factor(MARRY)5 0.2860587 1.3311706 0.1451446 1.971
## as.factor(MARRY)6 0.2118413 1.2359517 0.0968661 2.187
## as.factor(MARRY)8 0.2599211 1.2968278 0.3364359 0.773
## BMI -0.0088376 0.9912013 0.0047916 -1.844
## AVGSMK 0.0214141 1.0216450 0.0015238 14.053
## as.factor(SIZE_CAT)Small town 0.0465273 1.0476267 0.0694580 0.670
## as.factor(SIZE_CAT)Medium city 0.0342347 1.0348274 0.0721842 0.474
## as.factor(SIZE_CAT)Large city 0.0017790 1.0017806 0.0533245 0.033
## Pr(>|z|)
## BOOZE 0.133093
## booze_2 0.004118 **
## SEX < 2e-16 ***
## AGEYRS < 2e-16 ***
## as.factor(RACE)2 0.641684
## as.factor(RACE)3 0.107911
## as.factor(EDUC_CAT)Some HS 0.963220
## as.factor(EDUC_CAT)HS Grad 0.197367
## as.factor(EDUC_CAT)Some College 0.008119 **
## as.factor(EDUC_CAT)College+ 0.000192 ***
## as.factor(MARRY)3 0.132191
## as.factor(MARRY)4 0.107025
## as.factor(MARRY)5 0.048741 *
## as.factor(MARRY)6 0.028746 *
## as.factor(MARRY)8 0.439775
## BMI 0.065127 .
## AVGSMK < 2e-16 ***
## as.factor(SIZE_CAT)Small town 0.502945
## as.factor(SIZE_CAT)Medium city 0.635309
## as.factor(SIZE_CAT)Large city 0.973387
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## BOOZE 0.9888 1.0114 0.9743 1.0035
## booze_2 1.0006 0.9994 1.0002 1.0009
## SEX 0.5431 1.8413 0.4929 0.5984
## AGEYRS 1.1002 0.9089 1.0942 1.1062
## as.factor(RACE)2 0.9656 1.0356 0.8332 1.1190
## as.factor(RACE)3 0.7261 1.3772 0.4915 1.0727
## as.factor(EDUC_CAT)Some HS 0.9971 1.0029 0.8827 1.1264
## as.factor(EDUC_CAT)HS Grad 0.9286 1.0769 0.8296 1.0393
## as.factor(EDUC_CAT)Some College 0.8038 1.2441 0.6837 0.9449
## as.factor(EDUC_CAT)College+ 0.7198 1.3893 0.6055 0.8556
## as.factor(MARRY)3 1.0992 0.9097 0.9718 1.2433
## as.factor(MARRY)4 1.1786 0.8485 0.9651 1.4393
## as.factor(MARRY)5 1.3312 0.7512 1.0016 1.7692
## as.factor(MARRY)6 1.2360 0.8091 1.0222 1.4944
## as.factor(MARRY)8 1.2968 0.7711 0.6707 2.5076
## BMI 0.9912 1.0089 0.9819 1.0006
## AVGSMK 1.0216 0.9788 1.0186 1.0247
## as.factor(SIZE_CAT)Small town 1.0476 0.9545 0.9143 1.2004
## as.factor(SIZE_CAT)Medium city 1.0348 0.9663 0.8983 1.1921
## as.factor(SIZE_CAT)Large city 1.0018 0.9982 0.9024 1.1121
##
## Concordance= 0.78 (se = 0.005 )
## Likelihood ratio test= 2226 on 20 df, p=<2e-16
## Wald test = 1609 on 20 df, p=<2e-16
## Score (logrank) test = 1881 on 20 df, p=<2e-16
### Compare linear vs quadratic
anova(cox_lin, model_quad)
## Analysis of Deviance Table
## Cox model: response is Surv(FU, DEATH)
## Model 1: ~ BOOZE + SEX + AGEYRS + as.factor(RACE) + as.factor(EDUC_CAT) + as.factor(MARRY) + BMI + AVGSMK + as.factor(SIZE_CAT)
## Model 2: ~ BOOZE + booze_2 + SEX + AGEYRS + as.factor(RACE) + as.factor(EDUC_CAT) + as.factor(MARRY) + BMI + AVGSMK + as.factor(SIZE_CAT)
## loglik Chisq Df Pr(>|Chi|)
## 1 -18038
## 2 -18035 6.4288 1 0.01123 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
### Model with BOOZE cubed
model_cubic <- coxph(Surv(FU, DEATH) ~ BOOZE + booze_2 + booze_3 + SEX + AGEYRS +
as.factor(RACE) + as.factor(EDUC_CAT) + as.factor(MARRY) + BMI +
AVGSMK + as.factor(SIZE_CAT), data = d, ties = 'efron')
summary(model_cubic)
## Call:
## coxph(formula = Surv(FU, DEATH) ~ BOOZE + booze_2 + booze_3 +
## SEX + AGEYRS + as.factor(RACE) + as.factor(EDUC_CAT) + as.factor(MARRY) +
## BMI + AVGSMK + as.factor(SIZE_CAT), data = d, ties = "efron")
##
## n= 9250, number of events= 2145
##
## coef exp(coef) se(coef) z
## BOOZE -2.898e-02 9.714e-01 1.331e-02 -2.178
## booze_2 1.912e-03 1.002e+00 8.903e-04 2.148
## booze_3 -1.935e-05 1.000e+00 1.327e-05 -1.458
## SEX -6.159e-01 5.401e-01 4.961e-02 -12.416
## AGEYRS 9.512e-02 1.100e+00 2.771e-03 34.321
## as.factor(RACE)2 -3.733e-02 9.634e-01 7.526e-02 -0.496
## as.factor(RACE)3 -3.220e-01 7.247e-01 1.991e-01 -1.617
## as.factor(EDUC_CAT)Some HS -3.025e-03 9.970e-01 6.223e-02 -0.049
## as.factor(EDUC_CAT)HS Grad -6.884e-02 9.335e-01 5.758e-02 -1.196
## as.factor(EDUC_CAT)Some College -2.098e-01 8.108e-01 8.269e-02 -2.537
## as.factor(EDUC_CAT)College+ -3.195e-01 7.265e-01 8.834e-02 -3.617
## as.factor(MARRY)3 9.199e-02 1.096e+00 6.283e-02 1.464
## as.factor(MARRY)4 1.605e-01 1.174e+00 1.020e-01 1.574
## as.factor(MARRY)5 2.850e-01 1.330e+00 1.451e-01 1.964
## as.factor(MARRY)6 2.095e-01 1.233e+00 9.688e-02 2.163
## as.factor(MARRY)8 2.534e-01 1.288e+00 3.365e-01 0.753
## BMI -9.002e-03 9.910e-01 4.792e-03 -1.879
## AVGSMK 2.148e-02 1.022e+00 1.522e-03 14.116
## as.factor(SIZE_CAT)Small town 4.760e-02 1.049e+00 6.944e-02 0.686
## as.factor(SIZE_CAT)Medium city 2.953e-02 1.030e+00 7.225e-02 0.409
## as.factor(SIZE_CAT)Large city -6.133e-03 9.939e-01 5.354e-02 -0.115
## Pr(>|z|)
## BOOZE 0.029431 *
## booze_2 0.031697 *
## booze_3 0.144918
## SEX < 2e-16 ***
## AGEYRS < 2e-16 ***
## as.factor(RACE)2 0.619852
## as.factor(RACE)3 0.105850
## as.factor(EDUC_CAT)Some HS 0.961230
## as.factor(EDUC_CAT)HS Grad 0.231875
## as.factor(EDUC_CAT)Some College 0.011191 *
## as.factor(EDUC_CAT)College+ 0.000298 ***
## as.factor(MARRY)3 0.143176
## as.factor(MARRY)4 0.115572
## as.factor(MARRY)5 0.049512 *
## as.factor(MARRY)6 0.030540 *
## as.factor(MARRY)8 0.451365
## BMI 0.060287 .
## AVGSMK < 2e-16 ***
## as.factor(SIZE_CAT)Small town 0.492980
## as.factor(SIZE_CAT)Medium city 0.682723
## as.factor(SIZE_CAT)Large city 0.908811
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## BOOZE 0.9714 1.0294 0.9464 0.9971
## booze_2 1.0019 0.9981 1.0002 1.0037
## booze_3 1.0000 1.0000 1.0000 1.0000
## SEX 0.5401 1.8514 0.4901 0.5953
## AGEYRS 1.0998 0.9093 1.0938 1.1058
## as.factor(RACE)2 0.9634 1.0380 0.8312 1.1165
## as.factor(RACE)3 0.7247 1.3798 0.4906 1.0706
## as.factor(EDUC_CAT)Some HS 0.9970 1.0030 0.8825 1.1263
## as.factor(EDUC_CAT)HS Grad 0.9335 1.0713 0.8339 1.0450
## as.factor(EDUC_CAT)Some College 0.8108 1.2334 0.6895 0.9534
## as.factor(EDUC_CAT)College+ 0.7265 1.3765 0.6110 0.8638
## as.factor(MARRY)3 1.0964 0.9121 0.9693 1.2400
## as.factor(MARRY)4 1.1741 0.8517 0.9614 1.4338
## as.factor(MARRY)5 1.3298 0.7520 1.0006 1.7673
## as.factor(MARRY)6 1.2331 0.8110 1.0199 1.4910
## as.factor(MARRY)8 1.2884 0.7762 0.6663 2.4914
## BMI 0.9910 1.0090 0.9818 1.0004
## AVGSMK 1.0217 0.9787 1.0187 1.0248
## as.factor(SIZE_CAT)Small town 1.0488 0.9535 0.9153 1.2017
## as.factor(SIZE_CAT)Medium city 1.0300 0.9709 0.8940 1.1867
## as.factor(SIZE_CAT)Large city 0.9939 1.0062 0.8949 1.1039
##
## Concordance= 0.78 (se = 0.005 )
## Likelihood ratio test= 2228 on 21 df, p=<2e-16
## Wald test = 1617 on 21 df, p=<2e-16
## Score (logrank) test = 1891 on 21 df, p=<2e-16
### Compare linear vs cubic
anova(cox_lin, model_cubic)
## Analysis of Deviance Table
## Cox model: response is Surv(FU, DEATH)
## Model 1: ~ BOOZE + SEX + AGEYRS + as.factor(RACE) + as.factor(EDUC_CAT) + as.factor(MARRY) + BMI + AVGSMK + as.factor(SIZE_CAT)
## Model 2: ~ BOOZE + booze_2 + booze_3 + SEX + AGEYRS + as.factor(RACE) + as.factor(EDUC_CAT) + as.factor(MARRY) + BMI + AVGSMK + as.factor(SIZE_CAT)
## loglik Chisq Df Pr(>|Chi|)
## 1 -18038
## 2 -18034 9.3269 2 0.009434 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#Adjusted Cox regression with SEX
cox <- coxph(Surv(FU, DEATH) ~ as.factor(BOOZE_q) + SEX + AGEYRS +
as.factor(RACE) + as.factor(EDUC_CAT) + as.factor(MARRY) + BMI +
AVGSMK + as.factor(SIZE_CAT), data = d, ties='efron')
summary(cox)
## Call:
## coxph(formula = Surv(FU, DEATH) ~ as.factor(BOOZE_q) + SEX +
## AGEYRS + as.factor(RACE) + as.factor(EDUC_CAT) + as.factor(MARRY) +
## BMI + AVGSMK + as.factor(SIZE_CAT), data = d, ties = "efron")
##
## n= 9250, number of events= 2145
##
## coef exp(coef) se(coef) z
## as.factor(BOOZE_q)0–0.5/week 0.0009412 1.0009416 0.0757507 0.012
## as.factor(BOOZE_q)0.5–2/week -0.1644537 0.8483570 0.0653782 -2.515
## as.factor(BOOZE_q)>2/week -0.1056287 0.8997587 0.0582319 -1.814
## SEX -0.6351905 0.5298345 0.0497191 -12.776
## AGEYRS 0.0946933 1.0993217 0.0027755 34.118
## as.factor(RACE)2 -0.0430909 0.9578244 0.0753213 -0.572
## as.factor(RACE)3 -0.3442268 0.7087681 0.1992368 -1.728
## as.factor(EDUC_CAT)Some HS 0.0042420 1.0042510 0.0621828 0.068
## as.factor(EDUC_CAT)HS Grad -0.0616603 0.9402022 0.0576978 -1.069
## as.factor(EDUC_CAT)Some College -0.2046550 0.8149284 0.0828308 -2.471
## as.factor(EDUC_CAT)College+ -0.3173947 0.7280433 0.0884025 -3.590
## as.factor(MARRY)3 0.0994564 1.1045703 0.0628812 1.582
## as.factor(MARRY)4 0.1603845 1.1739622 0.1020528 1.572
## as.factor(MARRY)5 0.2896492 1.3359587 0.1451368 1.996
## as.factor(MARRY)6 0.2144900 1.2392297 0.0968847 2.214
## as.factor(MARRY)8 0.2506990 1.2849233 0.3364712 0.745
## BMI -0.0089585 0.9910815 0.0047857 -1.872
## AVGSMK 0.0215961 1.0218310 0.0015225 14.185
## as.factor(SIZE_CAT)Small town 0.0420737 1.0429714 0.0694660 0.606
## as.factor(SIZE_CAT)Medium city 0.0185098 1.0186822 0.0724548 0.255
## as.factor(SIZE_CAT)Large city -0.0204642 0.9797437 0.0540369 -0.379
## Pr(>|z|)
## as.factor(BOOZE_q)0–0.5/week 0.99009
## as.factor(BOOZE_q)0.5–2/week 0.01189 *
## as.factor(BOOZE_q)>2/week 0.06969 .
## SEX < 2e-16 ***
## AGEYRS < 2e-16 ***
## as.factor(RACE)2 0.56726
## as.factor(RACE)3 0.08404 .
## as.factor(EDUC_CAT)Some HS 0.94561
## as.factor(EDUC_CAT)HS Grad 0.28522
## as.factor(EDUC_CAT)Some College 0.01348 *
## as.factor(EDUC_CAT)College+ 0.00033 ***
## as.factor(MARRY)3 0.11373
## as.factor(MARRY)4 0.11605
## as.factor(MARRY)5 0.04597 *
## as.factor(MARRY)6 0.02684 *
## as.factor(MARRY)8 0.45622
## BMI 0.06122 .
## AVGSMK < 2e-16 ***
## as.factor(SIZE_CAT)Small town 0.54473
## as.factor(SIZE_CAT)Medium city 0.79836
## as.factor(SIZE_CAT)Large city 0.70490
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## as.factor(BOOZE_q)0–0.5/week 1.0009 0.9991 0.8628 1.1611
## as.factor(BOOZE_q)0.5–2/week 0.8484 1.1787 0.7463 0.9643
## as.factor(BOOZE_q)>2/week 0.8998 1.1114 0.8027 1.0085
## SEX 0.5298 1.8874 0.4806 0.5841
## AGEYRS 1.0993 0.9097 1.0934 1.1053
## as.factor(RACE)2 0.9578 1.0440 0.8264 1.1102
## as.factor(RACE)3 0.7088 1.4109 0.4796 1.0474
## as.factor(EDUC_CAT)Some HS 1.0043 0.9958 0.8890 1.1344
## as.factor(EDUC_CAT)HS Grad 0.9402 1.0636 0.8397 1.0528
## as.factor(EDUC_CAT)Some College 0.8149 1.2271 0.6928 0.9586
## as.factor(EDUC_CAT)College+ 0.7280 1.3735 0.6122 0.8658
## as.factor(MARRY)3 1.1046 0.9053 0.9765 1.2494
## as.factor(MARRY)4 1.1740 0.8518 0.9611 1.4339
## as.factor(MARRY)5 1.3360 0.7485 1.0052 1.7756
## as.factor(MARRY)6 1.2392 0.8070 1.0249 1.4984
## as.factor(MARRY)8 1.2849 0.7783 0.6645 2.4847
## BMI 0.9911 1.0090 0.9818 1.0004
## AVGSMK 1.0218 0.9786 1.0188 1.0249
## as.factor(SIZE_CAT)Small town 1.0430 0.9588 0.9102 1.1951
## as.factor(SIZE_CAT)Medium city 1.0187 0.9817 0.8838 1.1741
## as.factor(SIZE_CAT)Large city 0.9797 1.0207 0.8813 1.0892
##
## Concordance= 0.78 (se = 0.005 )
## Likelihood ratio test= 2226 on 21 df, p=<2e-16
## Wald test = 1614 on 21 df, p=<2e-16
## Score (logrank) test = 1884 on 21 df, p=<2e-16
cox.zph(cox)
## chisq df p
## as.factor(BOOZE_q) 12.05 3 0.0072
## SEX 5.21 1 0.0224
## AGEYRS 2.13 1 0.1440
## as.factor(RACE) 2.34 2 0.3102
## as.factor(EDUC_CAT) 10.75 4 0.0295
## as.factor(MARRY) 2.73 5 0.7424
## BMI 0.91 1 0.3401
## AVGSMK 1.07 1 0.3019
## as.factor(SIZE_CAT) 4.65 3 0.1990
## GLOBAL 36.60 21 0.0187
plot(cox.zph(cox))
#Product term with SEX Cox regression
##Categorical BOOZE
cox_product <- coxph(Surv(FU, DEATH) ~ as.factor(BOOZE_q)*SEX + AGEYRS +
as.factor(RACE) + as.factor(EDUC_CAT) + as.factor(MARRY) + BMI +
AVGSMK + as.factor(SIZE_CAT), data = d, ties = "efron")
summary(cox_product)
## Call:
## coxph(formula = Surv(FU, DEATH) ~ as.factor(BOOZE_q) * SEX +
## AGEYRS + as.factor(RACE) + as.factor(EDUC_CAT) + as.factor(MARRY) +
## BMI + AVGSMK + as.factor(SIZE_CAT), data = d, ties = "efron")
##
## n= 9250, number of events= 2145
##
## coef exp(coef) se(coef) z Pr(>|z|)
## as.factor(BOOZE_q)0–0.5/week 0.078561 1.081729 0.236440 0.332 0.73969
## as.factor(BOOZE_q)0.5–2/week -0.181241 0.834234 0.194007 -0.934 0.35020
## as.factor(BOOZE_q)>2/week -0.236482 0.789400 0.166699 -1.419 0.15601
## SEX -0.651654 0.521183 0.064827 -10.052 < 2e-16
## AGEYRS 0.094710 1.099340 0.002777 34.106 < 2e-16
## as.factor(RACE)2 -0.043171 0.957748 0.075375 -0.573 0.56682
## as.factor(RACE)3 -0.344107 0.708853 0.199241 -1.727 0.08415
## as.factor(EDUC_CAT)Some HS 0.004468 1.004478 0.062187 0.072 0.94273
## as.factor(EDUC_CAT)HS Grad -0.061866 0.940009 0.057731 -1.072 0.28389
## as.factor(EDUC_CAT)Some College -0.205913 0.813904 0.082887 -2.484 0.01298
## as.factor(EDUC_CAT)College+ -0.319003 0.726873 0.088438 -3.607 0.00031
## as.factor(MARRY)3 0.101557 1.106893 0.062921 1.614 0.10652
## as.factor(MARRY)4 0.158329 1.171552 0.102083 1.551 0.12090
## as.factor(MARRY)5 0.293233 1.340755 0.145186 2.020 0.04341
## as.factor(MARRY)6 0.215696 1.240726 0.096902 2.226 0.02602
## as.factor(MARRY)8 0.251173 1.285533 0.336770 0.746 0.45577
## BMI -0.008623 0.991414 0.004812 -1.792 0.07317
## AVGSMK 0.021598 1.021833 0.001523 14.184 < 2e-16
## as.factor(SIZE_CAT)Small town 0.043400 1.044355 0.069478 0.625 0.53220
## as.factor(SIZE_CAT)Medium city 0.019116 1.019300 0.072460 0.264 0.79192
## as.factor(SIZE_CAT)Large city -0.020824 0.979391 0.054054 -0.385 0.70005
## as.factor(BOOZE_q)0–0.5/week:SEX -0.052104 0.949230 0.150466 -0.346 0.72913
## as.factor(BOOZE_q)0.5–2/week:SEX 0.010711 1.010768 0.131179 0.082 0.93493
## as.factor(BOOZE_q)>2/week:SEX 0.103829 1.109411 0.120335 0.863 0.38823
##
## as.factor(BOOZE_q)0–0.5/week
## as.factor(BOOZE_q)0.5–2/week
## as.factor(BOOZE_q)>2/week
## SEX ***
## AGEYRS ***
## as.factor(RACE)2
## as.factor(RACE)3 .
## as.factor(EDUC_CAT)Some HS
## as.factor(EDUC_CAT)HS Grad
## as.factor(EDUC_CAT)Some College *
## as.factor(EDUC_CAT)College+ ***
## as.factor(MARRY)3
## as.factor(MARRY)4
## as.factor(MARRY)5 *
## as.factor(MARRY)6 *
## as.factor(MARRY)8
## BMI .
## AVGSMK ***
## as.factor(SIZE_CAT)Small town
## as.factor(SIZE_CAT)Medium city
## as.factor(SIZE_CAT)Large city
## as.factor(BOOZE_q)0–0.5/week:SEX
## as.factor(BOOZE_q)0.5–2/week:SEX
## as.factor(BOOZE_q)>2/week:SEX
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## as.factor(BOOZE_q)0–0.5/week 1.0817 0.9244 0.6806 1.7194
## as.factor(BOOZE_q)0.5–2/week 0.8342 1.1987 0.5704 1.2202
## as.factor(BOOZE_q)>2/week 0.7894 1.2668 0.5694 1.0944
## SEX 0.5212 1.9187 0.4590 0.5918
## AGEYRS 1.0993 0.9096 1.0934 1.1053
## as.factor(RACE)2 0.9577 1.0441 0.8262 1.1102
## as.factor(RACE)3 0.7089 1.4107 0.4797 1.0475
## as.factor(EDUC_CAT)Some HS 1.0045 0.9955 0.8892 1.1347
## as.factor(EDUC_CAT)HS Grad 0.9400 1.0638 0.8394 1.0526
## as.factor(EDUC_CAT)Some College 0.8139 1.2286 0.6919 0.9575
## as.factor(EDUC_CAT)College+ 0.7269 1.3758 0.6112 0.8644
## as.factor(MARRY)3 1.1069 0.9034 0.9785 1.2522
## as.factor(MARRY)4 1.1716 0.8536 0.9591 1.4310
## as.factor(MARRY)5 1.3408 0.7458 1.0087 1.7821
## as.factor(MARRY)6 1.2407 0.8060 1.0261 1.5002
## as.factor(MARRY)8 1.2855 0.7779 0.6644 2.4874
## BMI 0.9914 1.0087 0.9821 1.0008
## AVGSMK 1.0218 0.9786 1.0188 1.0249
## as.factor(SIZE_CAT)Small town 1.0444 0.9575 0.9114 1.1967
## as.factor(SIZE_CAT)Medium city 1.0193 0.9811 0.8843 1.1748
## as.factor(SIZE_CAT)Large city 0.9794 1.0210 0.8809 1.0888
## as.factor(BOOZE_q)0–0.5/week:SEX 0.9492 1.0535 0.7068 1.2748
## as.factor(BOOZE_q)0.5–2/week:SEX 1.0108 0.9893 0.7816 1.3071
## as.factor(BOOZE_q)>2/week:SEX 1.1094 0.9014 0.8763 1.4045
##
## Concordance= 0.78 (se = 0.005 )
## Likelihood ratio test= 2227 on 24 df, p=<2e-16
## Wald test = 1617 on 24 df, p=<2e-16
## Score (logrank) test = 1897 on 24 df, p=<2e-16
cox.zph(cox_product)
## chisq df p
## as.factor(BOOZE_q) 11.911 3 0.0077
## SEX 5.192 1 0.0227
## AGEYRS 2.122 1 0.1452
## as.factor(RACE) 2.359 2 0.3075
## as.factor(EDUC_CAT) 10.722 4 0.0299
## as.factor(MARRY) 2.727 5 0.7420
## BMI 0.898 1 0.3434
## AVGSMK 1.051 1 0.3054
## as.factor(SIZE_CAT) 4.647 3 0.1996
## as.factor(BOOZE_q):SEX 15.535 3 0.0014
## GLOBAL 36.895 24 0.0448
plot(cox.zph(cox_product))
##Continuous BOOZE
cox_product1 <- coxph(Surv(FU, DEATH) ~ BOOZE*SEX + AGEYRS +
as.factor(RACE) + as.factor(EDUC_CAT) + as.factor(MARRY) + BMI +
AVGSMK + as.factor(SIZE_CAT), data = d, ties = "efron")
summary(cox_product)
## Call:
## coxph(formula = Surv(FU, DEATH) ~ as.factor(BOOZE_q) * SEX +
## AGEYRS + as.factor(RACE) + as.factor(EDUC_CAT) + as.factor(MARRY) +
## BMI + AVGSMK + as.factor(SIZE_CAT), data = d, ties = "efron")
##
## n= 9250, number of events= 2145
##
## coef exp(coef) se(coef) z Pr(>|z|)
## as.factor(BOOZE_q)0–0.5/week 0.078561 1.081729 0.236440 0.332 0.73969
## as.factor(BOOZE_q)0.5–2/week -0.181241 0.834234 0.194007 -0.934 0.35020
## as.factor(BOOZE_q)>2/week -0.236482 0.789400 0.166699 -1.419 0.15601
## SEX -0.651654 0.521183 0.064827 -10.052 < 2e-16
## AGEYRS 0.094710 1.099340 0.002777 34.106 < 2e-16
## as.factor(RACE)2 -0.043171 0.957748 0.075375 -0.573 0.56682
## as.factor(RACE)3 -0.344107 0.708853 0.199241 -1.727 0.08415
## as.factor(EDUC_CAT)Some HS 0.004468 1.004478 0.062187 0.072 0.94273
## as.factor(EDUC_CAT)HS Grad -0.061866 0.940009 0.057731 -1.072 0.28389
## as.factor(EDUC_CAT)Some College -0.205913 0.813904 0.082887 -2.484 0.01298
## as.factor(EDUC_CAT)College+ -0.319003 0.726873 0.088438 -3.607 0.00031
## as.factor(MARRY)3 0.101557 1.106893 0.062921 1.614 0.10652
## as.factor(MARRY)4 0.158329 1.171552 0.102083 1.551 0.12090
## as.factor(MARRY)5 0.293233 1.340755 0.145186 2.020 0.04341
## as.factor(MARRY)6 0.215696 1.240726 0.096902 2.226 0.02602
## as.factor(MARRY)8 0.251173 1.285533 0.336770 0.746 0.45577
## BMI -0.008623 0.991414 0.004812 -1.792 0.07317
## AVGSMK 0.021598 1.021833 0.001523 14.184 < 2e-16
## as.factor(SIZE_CAT)Small town 0.043400 1.044355 0.069478 0.625 0.53220
## as.factor(SIZE_CAT)Medium city 0.019116 1.019300 0.072460 0.264 0.79192
## as.factor(SIZE_CAT)Large city -0.020824 0.979391 0.054054 -0.385 0.70005
## as.factor(BOOZE_q)0–0.5/week:SEX -0.052104 0.949230 0.150466 -0.346 0.72913
## as.factor(BOOZE_q)0.5–2/week:SEX 0.010711 1.010768 0.131179 0.082 0.93493
## as.factor(BOOZE_q)>2/week:SEX 0.103829 1.109411 0.120335 0.863 0.38823
##
## as.factor(BOOZE_q)0–0.5/week
## as.factor(BOOZE_q)0.5–2/week
## as.factor(BOOZE_q)>2/week
## SEX ***
## AGEYRS ***
## as.factor(RACE)2
## as.factor(RACE)3 .
## as.factor(EDUC_CAT)Some HS
## as.factor(EDUC_CAT)HS Grad
## as.factor(EDUC_CAT)Some College *
## as.factor(EDUC_CAT)College+ ***
## as.factor(MARRY)3
## as.factor(MARRY)4
## as.factor(MARRY)5 *
## as.factor(MARRY)6 *
## as.factor(MARRY)8
## BMI .
## AVGSMK ***
## as.factor(SIZE_CAT)Small town
## as.factor(SIZE_CAT)Medium city
## as.factor(SIZE_CAT)Large city
## as.factor(BOOZE_q)0–0.5/week:SEX
## as.factor(BOOZE_q)0.5–2/week:SEX
## as.factor(BOOZE_q)>2/week:SEX
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## as.factor(BOOZE_q)0–0.5/week 1.0817 0.9244 0.6806 1.7194
## as.factor(BOOZE_q)0.5–2/week 0.8342 1.1987 0.5704 1.2202
## as.factor(BOOZE_q)>2/week 0.7894 1.2668 0.5694 1.0944
## SEX 0.5212 1.9187 0.4590 0.5918
## AGEYRS 1.0993 0.9096 1.0934 1.1053
## as.factor(RACE)2 0.9577 1.0441 0.8262 1.1102
## as.factor(RACE)3 0.7089 1.4107 0.4797 1.0475
## as.factor(EDUC_CAT)Some HS 1.0045 0.9955 0.8892 1.1347
## as.factor(EDUC_CAT)HS Grad 0.9400 1.0638 0.8394 1.0526
## as.factor(EDUC_CAT)Some College 0.8139 1.2286 0.6919 0.9575
## as.factor(EDUC_CAT)College+ 0.7269 1.3758 0.6112 0.8644
## as.factor(MARRY)3 1.1069 0.9034 0.9785 1.2522
## as.factor(MARRY)4 1.1716 0.8536 0.9591 1.4310
## as.factor(MARRY)5 1.3408 0.7458 1.0087 1.7821
## as.factor(MARRY)6 1.2407 0.8060 1.0261 1.5002
## as.factor(MARRY)8 1.2855 0.7779 0.6644 2.4874
## BMI 0.9914 1.0087 0.9821 1.0008
## AVGSMK 1.0218 0.9786 1.0188 1.0249
## as.factor(SIZE_CAT)Small town 1.0444 0.9575 0.9114 1.1967
## as.factor(SIZE_CAT)Medium city 1.0193 0.9811 0.8843 1.1748
## as.factor(SIZE_CAT)Large city 0.9794 1.0210 0.8809 1.0888
## as.factor(BOOZE_q)0–0.5/week:SEX 0.9492 1.0535 0.7068 1.2748
## as.factor(BOOZE_q)0.5–2/week:SEX 1.0108 0.9893 0.7816 1.3071
## as.factor(BOOZE_q)>2/week:SEX 1.1094 0.9014 0.8763 1.4045
##
## Concordance= 0.78 (se = 0.005 )
## Likelihood ratio test= 2227 on 24 df, p=<2e-16
## Wald test = 1617 on 24 df, p=<2e-16
## Score (logrank) test = 1897 on 24 df, p=<2e-16
cox.zph(cox_product1)
## chisq df p
## BOOZE 6.705 1 0.0096
## SEX 5.273 1 0.0217
## AGEYRS 2.191 1 0.1389
## as.factor(RACE) 2.304 2 0.3161
## as.factor(EDUC_CAT) 10.653 4 0.0307
## as.factor(MARRY) 2.792 5 0.7320
## BMI 0.876 1 0.3493
## AVGSMK 1.047 1 0.3061
## as.factor(SIZE_CAT) 4.627 3 0.2012
## BOOZE:SEX 8.921 1 0.0028
## GLOBAL 32.597 20 0.0373
plot(cox.zph(cox_product))
#Stratified model by SEX
##Categorical BOOZE
cox_strata_cat <- coxph(Surv(FU, DEATH) ~ as.factor(BOOZE_q) + AGEYRS + as.factor(RACE) +
as.factor(EDUC_CAT) + as.factor(MARRY) + BMI +
AVGSMK + as.factor(SIZE_CAT) + strata(SEX), data = d, ties = 'efron')
summary(cox_strata_cat)
## Call:
## coxph(formula = Surv(FU, DEATH) ~ as.factor(BOOZE_q) + AGEYRS +
## as.factor(RACE) + as.factor(EDUC_CAT) + as.factor(MARRY) +
## BMI + AVGSMK + as.factor(SIZE_CAT) + strata(SEX), data = d,
## ties = "efron")
##
## n= 9250, number of events= 2145
##
## coef exp(coef) se(coef) z
## as.factor(BOOZE_q)0–0.5/week 0.0005982 1.0005984 0.0757495 0.008
## as.factor(BOOZE_q)0.5–2/week -0.1645524 0.8482733 0.0653731 -2.517
## as.factor(BOOZE_q)>2/week -0.1041216 0.9011157 0.0582166 -1.789
## AGEYRS 0.0944693 1.0990755 0.0027744 34.050
## as.factor(RACE)2 -0.0427210 0.9581787 0.0753318 -0.567
## as.factor(RACE)3 -0.3424003 0.7100639 0.1992445 -1.718
## as.factor(EDUC_CAT)Some HS 0.0054167 1.0054314 0.0621896 0.087
## as.factor(EDUC_CAT)HS Grad -0.0613644 0.9404804 0.0576961 -1.064
## as.factor(EDUC_CAT)Some College -0.2042635 0.8152475 0.0828347 -2.466
## as.factor(EDUC_CAT)College+ -0.3152965 0.7295726 0.0884112 -3.566
## as.factor(MARRY)3 0.1004634 1.1056832 0.0628689 1.598
## as.factor(MARRY)4 0.1585617 1.1718242 0.1020662 1.554
## as.factor(MARRY)5 0.2896007 1.3358940 0.1451502 1.995
## as.factor(MARRY)6 0.2132433 1.2376857 0.0968963 2.201
## as.factor(MARRY)8 0.2564887 1.2923842 0.3364842 0.762
## BMI -0.0089119 0.9911277 0.0047851 -1.862
## AVGSMK 0.0215104 1.0217434 0.0015226 14.127
## as.factor(SIZE_CAT)Small town 0.0415937 1.0424708 0.0694716 0.599
## as.factor(SIZE_CAT)Medium city 0.0170622 1.0172086 0.0724611 0.235
## as.factor(SIZE_CAT)Large city -0.0206079 0.9796030 0.0540481 -0.381
## Pr(>|z|)
## as.factor(BOOZE_q)0–0.5/week 0.993699
## as.factor(BOOZE_q)0.5–2/week 0.011832 *
## as.factor(BOOZE_q)>2/week 0.073692 .
## AGEYRS < 2e-16 ***
## as.factor(RACE)2 0.570643
## as.factor(RACE)3 0.085707 .
## as.factor(EDUC_CAT)Some HS 0.930592
## as.factor(EDUC_CAT)HS Grad 0.287519
## as.factor(EDUC_CAT)Some College 0.013666 *
## as.factor(EDUC_CAT)College+ 0.000362 ***
## as.factor(MARRY)3 0.110047
## as.factor(MARRY)4 0.120300
## as.factor(MARRY)5 0.046023 *
## as.factor(MARRY)6 0.027755 *
## as.factor(MARRY)8 0.445904
## BMI 0.062543 .
## AVGSMK < 2e-16 ***
## as.factor(SIZE_CAT)Small town 0.549363
## as.factor(SIZE_CAT)Medium city 0.813846
## as.factor(SIZE_CAT)Large city 0.702990
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## as.factor(BOOZE_q)0–0.5/week 1.0006 0.9994 0.8625 1.1607
## as.factor(BOOZE_q)0.5–2/week 0.8483 1.1789 0.7463 0.9642
## as.factor(BOOZE_q)>2/week 0.9011 1.1097 0.8039 1.0100
## AGEYRS 1.0991 0.9099 1.0931 1.1051
## as.factor(RACE)2 0.9582 1.0436 0.8267 1.1106
## as.factor(RACE)3 0.7101 1.4083 0.4805 1.0493
## as.factor(EDUC_CAT)Some HS 1.0054 0.9946 0.8901 1.1358
## as.factor(EDUC_CAT)HS Grad 0.9405 1.0633 0.8399 1.0531
## as.factor(EDUC_CAT)Some College 0.8152 1.2266 0.6931 0.9590
## as.factor(EDUC_CAT)College+ 0.7296 1.3707 0.6135 0.8676
## as.factor(MARRY)3 1.1057 0.9044 0.9775 1.2507
## as.factor(MARRY)4 1.1718 0.8534 0.9594 1.4313
## as.factor(MARRY)5 1.3359 0.7486 1.0051 1.7755
## as.factor(MARRY)6 1.2377 0.8080 1.0236 1.4965
## as.factor(MARRY)8 1.2924 0.7738 0.6683 2.4992
## BMI 0.9911 1.0090 0.9819 1.0005
## AVGSMK 1.0217 0.9787 1.0187 1.0248
## as.factor(SIZE_CAT)Small town 1.0425 0.9593 0.9098 1.1945
## as.factor(SIZE_CAT)Medium city 1.0172 0.9831 0.8825 1.1724
## as.factor(SIZE_CAT)Large city 0.9796 1.0208 0.8811 1.0891
##
## Concordance= 0.769 (se = 0.005 )
## Likelihood ratio test= 2057 on 20 df, p=<2e-16
## Wald test = 1440 on 20 df, p=<2e-16
## Score (logrank) test = 1722 on 20 df, p=<2e-16
cox.zph(cox_strata_cat)
## chisq df p
## as.factor(BOOZE_q) 17.11 3 0.00067
## AGEYRS 2.66 1 0.10275
## as.factor(RACE) 2.20 2 0.33283
## as.factor(EDUC_CAT) 10.19 4 0.03733
## as.factor(MARRY) 2.58 5 0.76512
## BMI 1.35 1 0.24580
## AVGSMK 1.85 1 0.17337
## as.factor(SIZE_CAT) 4.78 3 0.18882
## GLOBAL 31.60 20 0.04780
plot(cox.zph(cox_strata_cat))
cox_strata_cat1 <- coxph(Surv(FU, DEATH) ~ strata(BOOZE_q, SEX) + AGEYRS + as.factor(RACE) +
as.factor(EDUC_CAT) + as.factor(MARRY) + BMI +
AVGSMK + as.factor(SIZE_CAT), data = d, ties = 'efron')
summary(cox_strata_cat1)
## Call:
## coxph(formula = Surv(FU, DEATH) ~ strata(BOOZE_q, SEX) + AGEYRS +
## as.factor(RACE) + as.factor(EDUC_CAT) + as.factor(MARRY) +
## BMI + AVGSMK + as.factor(SIZE_CAT), data = d, ties = "efron")
##
## n= 9250, number of events= 2145
##
## coef exp(coef) se(coef) z Pr(>|z|)
## AGEYRS 0.094721 1.099352 0.002780 34.077 < 2e-16
## as.factor(RACE)2 -0.035983 0.964656 0.075454 -0.477 0.633439
## as.factor(RACE)3 -0.334183 0.715923 0.199265 -1.677 0.093527
## as.factor(EDUC_CAT)Some HS 0.008328 1.008363 0.062215 0.134 0.893516
## as.factor(EDUC_CAT)HS Grad -0.061364 0.940481 0.057737 -1.063 0.287868
## as.factor(EDUC_CAT)Some College -0.204930 0.814704 0.082938 -2.471 0.013478
## as.factor(EDUC_CAT)College+ -0.317439 0.728011 0.088442 -3.589 0.000332
## as.factor(MARRY)3 0.101433 1.106756 0.062945 1.611 0.107077
## as.factor(MARRY)4 0.157662 1.170771 0.102154 1.543 0.122740
## as.factor(MARRY)5 0.294478 1.342425 0.145248 2.027 0.042621
## as.factor(MARRY)6 0.214020 1.238647 0.096941 2.208 0.027263
## as.factor(MARRY)8 0.266243 1.305052 0.336924 0.790 0.429401
## BMI -0.008439 0.991596 0.004805 -1.756 0.079031
## AVGSMK 0.021585 1.021820 0.001524 14.163 < 2e-16
## as.factor(SIZE_CAT)Small town 0.044737 1.045753 0.069528 0.643 0.519937
## as.factor(SIZE_CAT)Medium city 0.018464 1.018636 0.072537 0.255 0.799071
## as.factor(SIZE_CAT)Large city -0.017115 0.983030 0.054166 -0.316 0.752017
##
## AGEYRS ***
## as.factor(RACE)2
## as.factor(RACE)3 .
## as.factor(EDUC_CAT)Some HS
## as.factor(EDUC_CAT)HS Grad
## as.factor(EDUC_CAT)Some College *
## as.factor(EDUC_CAT)College+ ***
## as.factor(MARRY)3
## as.factor(MARRY)4
## as.factor(MARRY)5 *
## as.factor(MARRY)6 *
## as.factor(MARRY)8
## BMI .
## AVGSMK ***
## as.factor(SIZE_CAT)Small town
## as.factor(SIZE_CAT)Medium city
## as.factor(SIZE_CAT)Large city
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## AGEYRS 1.0994 0.9096 1.0934 1.1054
## as.factor(RACE)2 0.9647 1.0366 0.8320 1.1184
## as.factor(RACE)3 0.7159 1.3968 0.4845 1.0580
## as.factor(EDUC_CAT)Some HS 1.0084 0.9917 0.8926 1.1391
## as.factor(EDUC_CAT)HS Grad 0.9405 1.0633 0.8399 1.0532
## as.factor(EDUC_CAT)Some College 0.8147 1.2274 0.6925 0.9585
## as.factor(EDUC_CAT)College+ 0.7280 1.3736 0.6121 0.8658
## as.factor(MARRY)3 1.1068 0.9035 0.9783 1.2521
## as.factor(MARRY)4 1.1708 0.8541 0.9583 1.4303
## as.factor(MARRY)5 1.3424 0.7449 1.0098 1.7845
## as.factor(MARRY)6 1.2386 0.8073 1.0243 1.4978
## as.factor(MARRY)8 1.3051 0.7663 0.6743 2.5259
## BMI 0.9916 1.0085 0.9823 1.0010
## AVGSMK 1.0218 0.9786 1.0188 1.0249
## as.factor(SIZE_CAT)Small town 1.0458 0.9562 0.9125 1.1984
## as.factor(SIZE_CAT)Medium city 1.0186 0.9817 0.8836 1.1743
## as.factor(SIZE_CAT)Large city 0.9830 1.0173 0.8840 1.0931
##
## Concordance= 0.756 (se = 0.005 )
## Likelihood ratio test= 1957 on 17 df, p=<2e-16
## Wald test = 1386 on 17 df, p=<2e-16
## Score (logrank) test = 1621 on 17 df, p=<2e-16
cox.zph(cox_strata_cat1)
## chisq df p
## AGEYRS 0.934 1 0.33
## as.factor(RACE) 1.339 2 0.51
## as.factor(EDUC_CAT) 5.184 4 0.27
## as.factor(MARRY) 2.654 5 0.75
## BMI 0.659 1 0.42
## AVGSMK 1.075 1 0.30
## as.factor(SIZE_CAT) 4.277 3 0.23
## GLOBAL 14.085 17 0.66
plot(cox.zph(cox_strata_cat1))
##Continuous BOOZE
cox_strata_lin <- coxph(Surv(FU, DEATH) ~ BOOZE + strata(SEX) + AGEYRS + as.factor(RACE) +
as.factor(EDUC_CAT) + as.factor(MARRY) + BMI +
AVGSMK + as.factor(SIZE_CAT), data = d, ties = 'efron')
summary(cox_strata_lin)
## Call:
## coxph(formula = Surv(FU, DEATH) ~ BOOZE + strata(SEX) + AGEYRS +
## as.factor(RACE) + as.factor(EDUC_CAT) + as.factor(MARRY) +
## BMI + AVGSMK + as.factor(SIZE_CAT), data = d, ties = "efron")
##
## n= 9250, number of events= 2145
##
## coef exp(coef) se(coef) z Pr(>|z|)
## BOOZE 0.004550 1.004560 0.004521 1.006 0.31420
## AGEYRS 0.095344 1.100037 0.002766 34.471 < 2e-16
## as.factor(RACE)2 -0.030171 0.970279 0.075247 -0.401 0.68845
## as.factor(RACE)3 -0.313876 0.730610 0.199098 -1.576 0.11491
## as.factor(EDUC_CAT)Some HS -0.008977 0.991063 0.062170 -0.144 0.88519
## as.factor(EDUC_CAT)HS Grad -0.084093 0.919346 0.057390 -1.465 0.14284
## as.factor(EDUC_CAT)Some College -0.233060 0.792106 0.082366 -2.830 0.00466
## as.factor(EDUC_CAT)College+ -0.344877 0.708308 0.087952 -3.921 8.81e-05
## as.factor(MARRY)3 0.096325 1.101117 0.062847 1.533 0.12535
## as.factor(MARRY)4 0.167296 1.182104 0.101947 1.641 0.10080
## as.factor(MARRY)5 0.281392 1.324973 0.145178 1.938 0.05259
## as.factor(MARRY)6 0.214993 1.239853 0.096863 2.220 0.02645
## as.factor(MARRY)8 0.276459 1.318452 0.336402 0.822 0.41119
## BMI -0.008214 0.991820 0.004784 -1.717 0.08597
## AVGSMK 0.021192 1.021418 0.001528 13.869 < 2e-16
## as.factor(SIZE_CAT)Small town 0.050405 1.051697 0.069423 0.726 0.46780
## as.factor(SIZE_CAT)Medium city 0.040288 1.041111 0.072116 0.559 0.57639
## as.factor(SIZE_CAT)Large city 0.014944 1.015056 0.053087 0.282 0.77832
##
## BOOZE
## AGEYRS ***
## as.factor(RACE)2
## as.factor(RACE)3
## as.factor(EDUC_CAT)Some HS
## as.factor(EDUC_CAT)HS Grad
## as.factor(EDUC_CAT)Some College **
## as.factor(EDUC_CAT)College+ ***
## as.factor(MARRY)3
## as.factor(MARRY)4
## as.factor(MARRY)5 .
## as.factor(MARRY)6 *
## as.factor(MARRY)8
## BMI .
## AVGSMK ***
## as.factor(SIZE_CAT)Small town
## as.factor(SIZE_CAT)Medium city
## as.factor(SIZE_CAT)Large city
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## BOOZE 1.0046 0.9955 0.9957 1.0135
## AGEYRS 1.1000 0.9091 1.0941 1.1060
## as.factor(RACE)2 0.9703 1.0306 0.8372 1.1245
## as.factor(RACE)3 0.7306 1.3687 0.4946 1.0793
## as.factor(EDUC_CAT)Some HS 0.9911 1.0090 0.8774 1.1195
## as.factor(EDUC_CAT)HS Grad 0.9193 1.0877 0.8215 1.0288
## as.factor(EDUC_CAT)Some College 0.7921 1.2625 0.6740 0.9309
## as.factor(EDUC_CAT)College+ 0.7083 1.4118 0.5962 0.8416
## as.factor(MARRY)3 1.1011 0.9082 0.9735 1.2455
## as.factor(MARRY)4 1.1821 0.8459 0.9680 1.4436
## as.factor(MARRY)5 1.3250 0.7547 0.9969 1.7611
## as.factor(MARRY)6 1.2399 0.8065 1.0255 1.4991
## as.factor(MARRY)8 1.3185 0.7585 0.6819 2.5492
## BMI 0.9918 1.0082 0.9826 1.0012
## AVGSMK 1.0214 0.9790 1.0184 1.0245
## as.factor(SIZE_CAT)Small town 1.0517 0.9508 0.9179 1.2050
## as.factor(SIZE_CAT)Medium city 1.0411 0.9605 0.9039 1.1992
## as.factor(SIZE_CAT)Large city 1.0151 0.9852 0.9148 1.1264
##
## Concordance= 0.768 (se = 0.005 )
## Likelihood ratio test= 2050 on 18 df, p=<2e-16
## Wald test = 1426 on 18 df, p=<2e-16
## Score (logrank) test = 1709 on 18 df, p=<2e-16
cox.zph(cox_strata_lin)
## chisq df p
## BOOZE 10.15 1 0.0014
## AGEYRS 2.72 1 0.0989
## as.factor(RACE) 2.16 2 0.3394
## as.factor(EDUC_CAT) 10.08 4 0.0392
## as.factor(MARRY) 2.62 5 0.7578
## BMI 1.30 1 0.2546
## AVGSMK 1.84 1 0.1751
## as.factor(SIZE_CAT) 4.77 3 0.1892
## GLOBAL 27.27 18 0.0740
plot(cox.zph(cox_strata_lin))
cox_strata_lin1 <- coxph(Surv(FU, DEATH) ~ strata(BOOZE, SEX) + AGEYRS + as.factor(RACE) +
as.factor(EDUC_CAT) + as.factor(MARRY) + BMI +
AVGSMK + as.factor(SIZE_CAT), data = d, ties = 'efron')
summary(cox_strata_lin1)
## Call:
## coxph(formula = Surv(FU, DEATH) ~ strata(BOOZE, SEX) + AGEYRS +
## as.factor(RACE) + as.factor(EDUC_CAT) + as.factor(MARRY) +
## BMI + AVGSMK + as.factor(SIZE_CAT), data = d, ties = "efron")
##
## n= 9250, number of events= 2145
##
## coef exp(coef) se(coef) z Pr(>|z|)
## AGEYRS 0.094241 1.098824 0.002813 33.507 < 2e-16
## as.factor(RACE)2 -0.055017 0.946469 0.076116 -0.723 0.469794
## as.factor(RACE)3 -0.383644 0.681374 0.200257 -1.916 0.055396
## as.factor(EDUC_CAT)Some HS 0.011292 1.011356 0.062950 0.179 0.857641
## as.factor(EDUC_CAT)HS Grad -0.057861 0.943781 0.058435 -0.990 0.322085
## as.factor(EDUC_CAT)Some College -0.198715 0.819783 0.083946 -2.367 0.017925
## as.factor(EDUC_CAT)College+ -0.314775 0.729953 0.090355 -3.484 0.000494
## as.factor(MARRY)3 0.102073 1.107464 0.063936 1.596 0.110380
## as.factor(MARRY)4 0.142776 1.153472 0.103765 1.376 0.168835
## as.factor(MARRY)5 0.323350 1.381749 0.146172 2.212 0.026958
## as.factor(MARRY)6 0.218940 1.244757 0.098249 2.228 0.025853
## as.factor(MARRY)8 0.283854 1.328239 0.338939 0.837 0.402323
## BMI -0.007542 0.992486 0.004851 -1.555 0.119983
## AVGSMK 0.021261 1.021489 0.001556 13.665 < 2e-16
## as.factor(SIZE_CAT)Small town 0.034391 1.034989 0.070484 0.488 0.625605
## as.factor(SIZE_CAT)Medium city -0.002916 0.997088 0.073392 -0.040 0.968307
## as.factor(SIZE_CAT)Large city -0.039409 0.961358 0.054723 -0.720 0.471432
##
## AGEYRS ***
## as.factor(RACE)2
## as.factor(RACE)3 .
## as.factor(EDUC_CAT)Some HS
## as.factor(EDUC_CAT)HS Grad
## as.factor(EDUC_CAT)Some College *
## as.factor(EDUC_CAT)College+ ***
## as.factor(MARRY)3
## as.factor(MARRY)4
## as.factor(MARRY)5 *
## as.factor(MARRY)6 *
## as.factor(MARRY)8
## BMI
## AVGSMK ***
## as.factor(SIZE_CAT)Small town
## as.factor(SIZE_CAT)Medium city
## as.factor(SIZE_CAT)Large city
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## AGEYRS 1.0988 0.9101 1.0928 1.1049
## as.factor(RACE)2 0.9465 1.0566 0.8153 1.0987
## as.factor(RACE)3 0.6814 1.4676 0.4602 1.0089
## as.factor(EDUC_CAT)Some HS 1.0114 0.9888 0.8940 1.1442
## as.factor(EDUC_CAT)HS Grad 0.9438 1.0596 0.8416 1.0583
## as.factor(EDUC_CAT)Some College 0.8198 1.2198 0.6954 0.9664
## as.factor(EDUC_CAT)College+ 0.7300 1.3700 0.6115 0.8714
## as.factor(MARRY)3 1.1075 0.9030 0.9770 1.2553
## as.factor(MARRY)4 1.1535 0.8669 0.9412 1.4136
## as.factor(MARRY)5 1.3817 0.7237 1.0375 1.8401
## as.factor(MARRY)6 1.2448 0.8034 1.0267 1.5091
## as.factor(MARRY)8 1.3282 0.7529 0.6836 2.5810
## BMI 0.9925 1.0076 0.9831 1.0020
## AVGSMK 1.0215 0.9790 1.0184 1.0246
## as.factor(SIZE_CAT)Small town 1.0350 0.9662 0.9014 1.1883
## as.factor(SIZE_CAT)Medium city 0.9971 1.0029 0.8635 1.1513
## as.factor(SIZE_CAT)Large city 0.9614 1.0402 0.8636 1.0702
##
## Concordance= 0.743 (se = 0.007 )
## Likelihood ratio test= 1886 on 17 df, p=<2e-16
## Wald test = 1329 on 17 df, p=<2e-16
## Score (logrank) test = 1555 on 17 df, p=<2e-16
cox.zph(cox_strata_lin1)
## chisq df p
## AGEYRS 0.690 1 0.41
## as.factor(RACE) 1.622 2 0.44
## as.factor(EDUC_CAT) 6.267 4 0.18
## as.factor(MARRY) 3.156 5 0.68
## BMI 0.494 1 0.48
## AVGSMK 0.928 1 0.34
## as.factor(SIZE_CAT) 4.369 3 0.22
## GLOBAL 15.499 17 0.56
plot(cox.zph(cox_strata_lin1))
###
cox_men <- coxph(Surv(FU, DEATH) ~ as.factor(BOOZE_q) +
AGEYRS + as.factor(RACE) + as.factor(EDUC_CAT) +
as.factor(MARRY) + BMI + AVGSMK + as.factor(SIZE_CAT),
data = d[d$SEX == 1, ])
summary(cox_men)
## Call:
## coxph(formula = Surv(FU, DEATH) ~ as.factor(BOOZE_q) + AGEYRS +
## as.factor(RACE) + as.factor(EDUC_CAT) + as.factor(MARRY) +
## BMI + AVGSMK + as.factor(SIZE_CAT), data = d[d$SEX == 1,
## ])
##
## n= 4349, number of events= 1258
##
## coef exp(coef) se(coef) z Pr(>|z|)
## as.factor(BOOZE_q)0–0.5/week 0.030921 1.031404 0.106069 0.292 0.770658
## as.factor(BOOZE_q)0.5–2/week -0.155038 0.856382 0.084044 -1.845 0.065076
## as.factor(BOOZE_q)>2/week -0.121964 0.885180 0.070185 -1.738 0.082253
## AGEYRS 0.094329 1.098921 0.003591 26.269 < 2e-16
## as.factor(RACE)2 -0.124606 0.882844 0.099122 -1.257 0.208717
## as.factor(RACE)3 -0.547403 0.578450 0.262715 -2.084 0.037193
## as.factor(EDUC_CAT)Some HS 0.041982 1.042875 0.080265 0.523 0.600949
## as.factor(EDUC_CAT)HS Grad -0.066953 0.935239 0.076742 -0.872 0.382965
## as.factor(EDUC_CAT)Some College -0.182969 0.832794 0.107612 -1.700 0.089081
## as.factor(EDUC_CAT)College+ -0.433200 0.648431 0.115983 -3.735 0.000188
## as.factor(MARRY)3 0.129384 1.138127 0.112721 1.148 0.251039
## as.factor(MARRY)4 0.389988 1.476963 0.130698 2.984 0.002846
## as.factor(MARRY)5 0.329985 1.390947 0.184067 1.793 0.073015
## as.factor(MARRY)6 0.341293 1.406765 0.125603 2.717 0.006583
## as.factor(MARRY)8 0.483891 1.622374 0.452610 1.069 0.285019
## BMI -0.018552 0.981619 0.007291 -2.545 0.010938
## AVGSMK 0.019458 1.019648 0.001869 10.412 < 2e-16
## as.factor(SIZE_CAT)Small town 0.049592 1.050843 0.092874 0.534 0.593357
## as.factor(SIZE_CAT)Medium city 0.109276 1.115470 0.096604 1.131 0.257979
## as.factor(SIZE_CAT)Large city -0.032266 0.968249 0.070377 -0.458 0.646617
##
## as.factor(BOOZE_q)0–0.5/week
## as.factor(BOOZE_q)0.5–2/week .
## as.factor(BOOZE_q)>2/week .
## AGEYRS ***
## as.factor(RACE)2
## as.factor(RACE)3 *
## as.factor(EDUC_CAT)Some HS
## as.factor(EDUC_CAT)HS Grad
## as.factor(EDUC_CAT)Some College .
## as.factor(EDUC_CAT)College+ ***
## as.factor(MARRY)3
## as.factor(MARRY)4 **
## as.factor(MARRY)5 .
## as.factor(MARRY)6 **
## as.factor(MARRY)8
## BMI *
## AVGSMK ***
## as.factor(SIZE_CAT)Small town
## as.factor(SIZE_CAT)Medium city
## as.factor(SIZE_CAT)Large city
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## as.factor(BOOZE_q)0–0.5/week 1.0314 0.9696 0.8378 1.2697
## as.factor(BOOZE_q)0.5–2/week 0.8564 1.1677 0.7263 1.0097
## as.factor(BOOZE_q)>2/week 0.8852 1.1297 0.7714 1.0157
## AGEYRS 1.0989 0.9100 1.0912 1.1067
## as.factor(RACE)2 0.8828 1.1327 0.7270 1.0722
## as.factor(RACE)3 0.5785 1.7288 0.3457 0.9680
## as.factor(EDUC_CAT)Some HS 1.0429 0.9589 0.8911 1.2205
## as.factor(EDUC_CAT)HS Grad 0.9352 1.0692 0.8046 1.0870
## as.factor(EDUC_CAT)Some College 0.8328 1.2008 0.6744 1.0283
## as.factor(EDUC_CAT)College+ 0.6484 1.5422 0.5166 0.8139
## as.factor(MARRY)3 1.1381 0.8786 0.9125 1.4195
## as.factor(MARRY)4 1.4770 0.6771 1.1432 1.9082
## as.factor(MARRY)5 1.3909 0.7189 0.9697 1.9952
## as.factor(MARRY)6 1.4068 0.7109 1.0998 1.7994
## as.factor(MARRY)8 1.6224 0.6164 0.6682 3.9392
## BMI 0.9816 1.0187 0.9677 0.9957
## AVGSMK 1.0196 0.9807 1.0159 1.0234
## as.factor(SIZE_CAT)Small town 1.0508 0.9516 0.8760 1.2606
## as.factor(SIZE_CAT)Medium city 1.1155 0.8965 0.9231 1.3480
## as.factor(SIZE_CAT)Large city 0.9682 1.0328 0.8435 1.1115
##
## Concordance= 0.776 (se = 0.006 )
## Likelihood ratio test= 1255 on 20 df, p=<2e-16
## Wald test = 883 on 20 df, p=<2e-16
## Score (logrank) test = 1063 on 20 df, p=<2e-16
cox.zph(cox_men)
## chisq df p
## as.factor(BOOZE_q) 1.14e+01 3 0.0098
## AGEYRS 5.50e+00 1 0.0190
## as.factor(RACE) 5.63e-01 2 0.7548
## as.factor(EDUC_CAT) 5.16e+00 4 0.2715
## as.factor(MARRY) 1.65e+00 5 0.8952
## BMI 6.99e-04 1 0.9789
## AVGSMK 5.39e-02 1 0.8164
## as.factor(SIZE_CAT) 3.73e+00 3 0.2925
## GLOBAL 2.26e+01 20 0.3106
plot(cox.zph(cox_men))
cox_women <- coxph(Surv(FU, DEATH) ~ as.factor(BOOZE_q) + AGEYRS +
as.factor(RACE) + as.factor(EDUC_CAT) + as.factor(MARRY) +
BMI + AVGSMK + as.factor(SIZE_CAT), data = d[d$SEX == 2, ])
summary(cox_women)
## Call:
## coxph(formula = Surv(FU, DEATH) ~ as.factor(BOOZE_q) + AGEYRS +
## as.factor(RACE) + as.factor(EDUC_CAT) + as.factor(MARRY) +
## BMI + AVGSMK + as.factor(SIZE_CAT), data = d[d$SEX == 2,
## ])
##
## n= 4901, number of events= 887
##
## coef exp(coef) se(coef) z
## as.factor(BOOZE_q)0–0.5/week -0.0334060 0.9671458 0.1087563 -0.307
## as.factor(BOOZE_q)0.5–2/week -0.1735664 0.8406614 0.1050143 -1.653
## as.factor(BOOZE_q)>2/week -0.0519451 0.9493809 0.1066823 -0.487
## AGEYRS 0.0951472 1.0998207 0.0044638 21.315
## as.factor(RACE)2 0.0581433 1.0598669 0.1180136 0.493
## as.factor(RACE)3 -0.0129767 0.9871071 0.3065457 -0.042
## as.factor(EDUC_CAT)Some HS -0.0408000 0.9600211 0.0986586 -0.414
## as.factor(EDUC_CAT)HS Grad -0.0325165 0.9680065 0.0885558 -0.367
## as.factor(EDUC_CAT)Some College -0.1861889 0.8301168 0.1308871 -1.423
## as.factor(EDUC_CAT)College+ -0.0707968 0.9316512 0.1386667 -0.511
## as.factor(MARRY)3 0.0633424 1.0653916 0.0780444 0.812
## as.factor(MARRY)4 -0.1531267 0.8580210 0.1626053 -0.942
## as.factor(MARRY)5 0.2341114 1.2637853 0.2382021 0.983
## as.factor(MARRY)6 -0.0202942 0.9799104 0.1557067 -0.130
## as.factor(MARRY)8 -0.0057576 0.9942590 0.5050423 -0.011
## BMI -0.0002604 0.9997396 0.0064150 -0.041
## AVGSMK 0.0263737 1.0267246 0.0026682 9.884
## as.factor(SIZE_CAT)Small town 0.0279975 1.0283931 0.1050527 0.267
## as.factor(SIZE_CAT)Medium city -0.0943020 0.9100079 0.1097958 -0.859
## as.factor(SIZE_CAT)Large city -0.0142740 0.9858274 0.0849779 -0.168
## Pr(>|z|)
## as.factor(BOOZE_q)0–0.5/week 0.7587
## as.factor(BOOZE_q)0.5–2/week 0.0984 .
## as.factor(BOOZE_q)>2/week 0.6263
## AGEYRS <2e-16 ***
## as.factor(RACE)2 0.6222
## as.factor(RACE)3 0.9662
## as.factor(EDUC_CAT)Some HS 0.6792
## as.factor(EDUC_CAT)HS Grad 0.7135
## as.factor(EDUC_CAT)Some College 0.1549
## as.factor(EDUC_CAT)College+ 0.6097
## as.factor(MARRY)3 0.4170
## as.factor(MARRY)4 0.3463
## as.factor(MARRY)5 0.3257
## as.factor(MARRY)6 0.8963
## as.factor(MARRY)8 0.9909
## BMI 0.9676
## AVGSMK <2e-16 ***
## as.factor(SIZE_CAT)Small town 0.7898
## as.factor(SIZE_CAT)Medium city 0.3904
## as.factor(SIZE_CAT)Large city 0.8666
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## as.factor(BOOZE_q)0–0.5/week 0.9671 1.0340 0.7815 1.197
## as.factor(BOOZE_q)0.5–2/week 0.8407 1.1895 0.6843 1.033
## as.factor(BOOZE_q)>2/week 0.9494 1.0533 0.7703 1.170
## AGEYRS 1.0998 0.9092 1.0902 1.109
## as.factor(RACE)2 1.0599 0.9435 0.8410 1.336
## as.factor(RACE)3 0.9871 1.0131 0.5413 1.800
## as.factor(EDUC_CAT)Some HS 0.9600 1.0416 0.7912 1.165
## as.factor(EDUC_CAT)HS Grad 0.9680 1.0331 0.8138 1.151
## as.factor(EDUC_CAT)Some College 0.8301 1.2046 0.6423 1.073
## as.factor(EDUC_CAT)College+ 0.9317 1.0734 0.7099 1.223
## as.factor(MARRY)3 1.0654 0.9386 0.9143 1.241
## as.factor(MARRY)4 0.8580 1.1655 0.6239 1.180
## as.factor(MARRY)5 1.2638 0.7913 0.7923 2.016
## as.factor(MARRY)6 0.9799 1.0205 0.7222 1.330
## as.factor(MARRY)8 0.9943 1.0058 0.3695 2.675
## BMI 0.9997 1.0003 0.9872 1.012
## AVGSMK 1.0267 0.9740 1.0214 1.032
## as.factor(SIZE_CAT)Small town 1.0284 0.9724 0.8370 1.264
## as.factor(SIZE_CAT)Medium city 0.9100 1.0989 0.7338 1.129
## as.factor(SIZE_CAT)Large city 0.9858 1.0144 0.8346 1.164
##
## Concordance= 0.764 (se = 0.007 )
## Likelihood ratio test= 828.2 on 20 df, p=<2e-16
## Wald test = 582.2 on 20 df, p=<2e-16
## Score (logrank) test = 687.7 on 20 df, p=<2e-16
cox.zph(cox_women)
## chisq df p
## as.factor(BOOZE_q) 5.9993 3 0.112
## AGEYRS 0.0715 1 0.789
## as.factor(RACE) 5.4630 2 0.065
## as.factor(EDUC_CAT) 6.1471 4 0.188
## as.factor(MARRY) 3.1200 5 0.681
## BMI 3.1380 1 0.076
## AVGSMK 4.2504 1 0.039
## as.factor(SIZE_CAT) 2.8907 3 0.409
## GLOBAL 23.1428 20 0.282
plot(cox.zph(cox_women))
cox_men_lin <- coxph(Surv(FU, DEATH) ~ BOOZE +
AGEYRS + as.factor(RACE) + as.factor(EDUC_CAT) +
as.factor(MARRY) + BMI + AVGSMK + as.factor(SIZE_CAT),
data = d[d$SEX == 1, ])
summary(cox_men_lin)
## Call:
## coxph(formula = Surv(FU, DEATH) ~ BOOZE + AGEYRS + as.factor(RACE) +
## as.factor(EDUC_CAT) + as.factor(MARRY) + BMI + AVGSMK + as.factor(SIZE_CAT),
## data = d[d$SEX == 1, ])
##
## n= 4349, number of events= 1258
##
## coef exp(coef) se(coef) z Pr(>|z|)
## BOOZE 0.004807 1.004819 0.004846 0.992 0.32118
## AGEYRS 0.095153 1.099827 0.003579 26.589 < 2e-16
## as.factor(RACE)2 -0.110161 0.895690 0.098953 -1.113 0.26559
## as.factor(RACE)3 -0.515484 0.597211 0.262527 -1.964 0.04958
## as.factor(EDUC_CAT)Some HS 0.022980 1.023247 0.080324 0.286 0.77480
## as.factor(EDUC_CAT)HS Grad -0.093860 0.910410 0.076414 -1.228 0.21933
## as.factor(EDUC_CAT)Some College -0.215128 0.806438 0.107099 -2.009 0.04457
## as.factor(EDUC_CAT)College+ -0.463280 0.629217 0.115535 -4.010 6.08e-05
## as.factor(MARRY)3 0.113628 1.120335 0.112818 1.007 0.31385
## as.factor(MARRY)4 0.399728 1.491419 0.130413 3.065 0.00218
## as.factor(MARRY)5 0.311784 1.365860 0.184113 1.693 0.09037
## as.factor(MARRY)6 0.339154 1.403760 0.125490 2.703 0.00688
## as.factor(MARRY)8 0.543790 1.722523 0.451993 1.203 0.22894
## BMI -0.018143 0.982020 0.007292 -2.488 0.01285
## AVGSMK 0.019091 1.019275 0.001873 10.195 < 2e-16
## as.factor(SIZE_CAT)Small town 0.056321 1.057937 0.092803 0.607 0.54392
## as.factor(SIZE_CAT)Medium city 0.135716 1.145356 0.096188 1.411 0.15826
## as.factor(SIZE_CAT)Large city 0.009956 1.010006 0.069101 0.144 0.88544
##
## BOOZE
## AGEYRS ***
## as.factor(RACE)2
## as.factor(RACE)3 *
## as.factor(EDUC_CAT)Some HS
## as.factor(EDUC_CAT)HS Grad
## as.factor(EDUC_CAT)Some College *
## as.factor(EDUC_CAT)College+ ***
## as.factor(MARRY)3
## as.factor(MARRY)4 **
## as.factor(MARRY)5 .
## as.factor(MARRY)6 **
## as.factor(MARRY)8
## BMI *
## AVGSMK ***
## as.factor(SIZE_CAT)Small town
## as.factor(SIZE_CAT)Medium city
## as.factor(SIZE_CAT)Large city
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## BOOZE 1.0048 0.9952 0.9953 1.0144
## AGEYRS 1.0998 0.9092 1.0921 1.1076
## as.factor(RACE)2 0.8957 1.1165 0.7378 1.0874
## as.factor(RACE)3 0.5972 1.6744 0.3570 0.9991
## as.factor(EDUC_CAT)Some HS 1.0232 0.9773 0.8742 1.1977
## as.factor(EDUC_CAT)HS Grad 0.9104 1.0984 0.7838 1.0575
## as.factor(EDUC_CAT)Some College 0.8064 1.2400 0.6537 0.9948
## as.factor(EDUC_CAT)College+ 0.6292 1.5893 0.5017 0.7891
## as.factor(MARRY)3 1.1203 0.8926 0.8981 1.3976
## as.factor(MARRY)4 1.4914 0.6705 1.1550 1.9258
## as.factor(MARRY)5 1.3659 0.7321 0.9521 1.9594
## as.factor(MARRY)6 1.4038 0.7124 1.0977 1.7952
## as.factor(MARRY)8 1.7225 0.5805 0.7103 4.1774
## BMI 0.9820 1.0183 0.9681 0.9962
## AVGSMK 1.0193 0.9811 1.0155 1.0230
## as.factor(SIZE_CAT)Small town 1.0579 0.9452 0.8820 1.2690
## as.factor(SIZE_CAT)Medium city 1.1454 0.8731 0.9486 1.3830
## as.factor(SIZE_CAT)Large city 1.0100 0.9901 0.8821 1.1565
##
## Concordance= 0.775 (se = 0.006 )
## Likelihood ratio test= 1250 on 18 df, p=<2e-16
## Wald test = 875.8 on 18 df, p=<2e-16
## Score (logrank) test = 1055 on 18 df, p=<2e-16
#cox.zph(cox_men_lin)
#plot(cox.zph(cox_men_lin))
cox_women_lin <- coxph(Surv(FU, DEATH) ~ BOOZE + AGEYRS +
as.factor(RACE) + as.factor(EDUC_CAT) + as.factor(MARRY) +
BMI + AVGSMK + as.factor(SIZE_CAT), data = d[d$SEX == 2, ])
summary(cox_women_lin)
## Call:
## coxph(formula = Surv(FU, DEATH) ~ BOOZE + AGEYRS + as.factor(RACE) +
## as.factor(EDUC_CAT) + as.factor(MARRY) + BMI + AVGSMK + as.factor(SIZE_CAT),
## data = d[d$SEX == 2, ])
##
## n= 4901, number of events= 887
##
## coef exp(coef) se(coef) z
## BOOZE -0.0004860 0.9995141 0.0126942 -0.038
## AGEYRS 0.0959040 1.1006534 0.0044463 21.570
## as.factor(RACE)2 0.0695187 1.0719921 0.1177918 0.590
## as.factor(RACE)3 0.0045725 1.0045830 0.3063751 0.015
## as.factor(EDUC_CAT)Some HS -0.0467198 0.9543548 0.0985417 -0.474
## as.factor(EDUC_CAT)HS Grad -0.0465275 0.9545383 0.0880187 -0.529
## as.factor(EDUC_CAT)Some College -0.2039894 0.8154710 0.1300834 -1.568
## as.factor(EDUC_CAT)College+ -0.0890168 0.9148302 0.1382296 -0.644
## as.factor(MARRY)3 0.0611352 1.0630426 0.0779948 0.784
## as.factor(MARRY)4 -0.1477257 0.8626677 0.1626036 -0.909
## as.factor(MARRY)5 0.2253535 1.2527655 0.2381139 0.946
## as.factor(MARRY)6 -0.0172190 0.9829284 0.1556646 -0.111
## as.factor(MARRY)8 -0.0303307 0.9701246 0.5046491 -0.060
## BMI 0.0001807 1.0001807 0.0063993 0.028
## AVGSMK 0.0262778 1.0266261 0.0026828 9.795
## as.factor(SIZE_CAT)Small town 0.0335570 1.0341264 0.1050501 0.319
## as.factor(SIZE_CAT)Medium city -0.0791027 0.9239451 0.1092792 -0.724
## as.factor(SIZE_CAT)Large city 0.0076774 1.0077070 0.0838334 0.092
## Pr(>|z|)
## BOOZE 0.969
## AGEYRS <2e-16 ***
## as.factor(RACE)2 0.555
## as.factor(RACE)3 0.988
## as.factor(EDUC_CAT)Some HS 0.635
## as.factor(EDUC_CAT)HS Grad 0.597
## as.factor(EDUC_CAT)Some College 0.117
## as.factor(EDUC_CAT)College+ 0.520
## as.factor(MARRY)3 0.433
## as.factor(MARRY)4 0.364
## as.factor(MARRY)5 0.344
## as.factor(MARRY)6 0.912
## as.factor(MARRY)8 0.952
## BMI 0.977
## AVGSMK <2e-16 ***
## as.factor(SIZE_CAT)Small town 0.749
## as.factor(SIZE_CAT)Medium city 0.469
## as.factor(SIZE_CAT)Large city 0.927
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## BOOZE 0.9995 1.0005 0.9750 1.025
## AGEYRS 1.1007 0.9086 1.0911 1.110
## as.factor(RACE)2 1.0720 0.9328 0.8510 1.350
## as.factor(RACE)3 1.0046 0.9954 0.5511 1.831
## as.factor(EDUC_CAT)Some HS 0.9544 1.0478 0.7867 1.158
## as.factor(EDUC_CAT)HS Grad 0.9545 1.0476 0.8033 1.134
## as.factor(EDUC_CAT)Some College 0.8155 1.2263 0.6319 1.052
## as.factor(EDUC_CAT)College+ 0.9148 1.0931 0.6977 1.200
## as.factor(MARRY)3 1.0630 0.9407 0.9123 1.239
## as.factor(MARRY)4 0.8627 1.1592 0.6272 1.186
## as.factor(MARRY)5 1.2528 0.7982 0.7856 1.998
## as.factor(MARRY)6 0.9829 1.0174 0.7245 1.334
## as.factor(MARRY)8 0.9701 1.0308 0.3608 2.608
## BMI 1.0002 0.9998 0.9877 1.013
## AVGSMK 1.0266 0.9741 1.0212 1.032
## as.factor(SIZE_CAT)Small town 1.0341 0.9670 0.8417 1.271
## as.factor(SIZE_CAT)Medium city 0.9239 1.0823 0.7458 1.145
## as.factor(SIZE_CAT)Large city 1.0077 0.9924 0.8550 1.188
##
## Concordance= 0.763 (se = 0.007 )
## Likelihood ratio test= 825.3 on 18 df, p=<2e-16
## Wald test = 576.3 on 18 df, p=<2e-16
## Score (logrank) test = 683 on 18 df, p=<2e-16
#cox.zph(cox_women_lin)
#plot(cox.zph(cox_women_lin))
#Kaplan
fit<-survfit(Surv(FU, DEATH)~BOOZE_q, data=d)
summary(fit)
## Call: survfit(formula = Surv(FU, DEATH) ~ BOOZE_q, data = d)
##
## BOOZE_q=0/week
## time n.risk n.event survival std.err lower 95% CI upper 95% CI
## 0.0000 4053 2 1.000 0.000349 0.999 1.000
## 0.0833 4051 3 0.999 0.000551 0.998 1.000
## 0.1667 4048 1 0.999 0.000604 0.997 1.000
## 0.2500 4047 4 0.998 0.000779 0.996 0.999
## 0.3333 4043 6 0.996 0.000985 0.994 0.998
## 0.4167 4037 2 0.996 0.001044 0.994 0.998
## 0.5000 4035 2 0.995 0.001101 0.993 0.997
## 0.5833 4033 7 0.993 0.001278 0.991 0.996
## 0.6667 4026 3 0.993 0.001346 0.990 0.995
## 0.7500 4023 3 0.992 0.001412 0.989 0.995
## 0.8333 4020 4 0.991 0.001494 0.988 0.994
## 0.9167 4016 1 0.991 0.001514 0.988 0.994
## 1.0000 4015 6 0.989 0.001628 0.986 0.992
## 1.0833 4009 5 0.988 0.001717 0.985 0.991
## 1.1667 4004 5 0.987 0.001801 0.983 0.990
## 1.2500 3999 5 0.985 0.001881 0.982 0.989
## 1.3333 3994 4 0.984 0.001943 0.981 0.988
## 1.4167 3990 4 0.983 0.002003 0.980 0.987
## 1.5000 3986 6 0.982 0.002089 0.978 0.986
## 1.5833 3980 4 0.981 0.002144 0.977 0.985
## 1.6667 3976 6 0.980 0.002225 0.975 0.984
## 1.7500 3970 10 0.977 0.002352 0.972 0.982
## 1.8333 3960 7 0.975 0.002437 0.971 0.980
## 1.9167 3953 6 0.974 0.002507 0.969 0.979
## 2.0000 3947 4 0.973 0.002552 0.968 0.978
## 2.0833 3943 4 0.972 0.002597 0.967 0.977
## 2.1667 3939 1 0.972 0.002608 0.967 0.977
## 2.2500 3938 7 0.970 0.002684 0.965 0.975
## 2.3333 3931 3 0.969 0.002716 0.964 0.974
## 2.4167 3928 7 0.967 0.002788 0.962 0.973
## 2.5000 3921 11 0.965 0.002898 0.959 0.970
## 2.5833 3910 8 0.963 0.002975 0.957 0.969
## 2.6667 3902 8 0.961 0.003050 0.955 0.967
## 2.7500 3894 2 0.960 0.003068 0.954 0.966
## 2.8333 3892 2 0.960 0.003086 0.954 0.966
## 2.9167 3890 7 0.958 0.003149 0.952 0.964
## 3.0000 3883 10 0.956 0.003236 0.949 0.962
## 3.0833 3873 6 0.954 0.003287 0.948 0.961
## 3.1667 3867 5 0.953 0.003329 0.946 0.959
## 3.2500 3862 6 0.951 0.003378 0.945 0.958
## 3.3333 3856 1 0.951 0.003386 0.945 0.958
## 3.4167 3855 6 0.950 0.003434 0.943 0.956
## 3.5000 3849 1 0.949 0.003442 0.943 0.956
## 3.5833 3848 5 0.948 0.003482 0.941 0.955
## 3.6667 3843 8 0.946 0.003544 0.939 0.953
## 3.7500 3835 10 0.944 0.003619 0.937 0.951
## 3.8333 3825 1 0.943 0.003627 0.936 0.951
## 3.9167 3824 6 0.942 0.003671 0.935 0.949
## 4.0000 3818 10 0.940 0.003743 0.932 0.947
## 4.0833 3808 8 0.938 0.003800 0.930 0.945
## 4.1667 3800 1 0.937 0.003807 0.930 0.945
## 4.2500 3799 3 0.937 0.003828 0.929 0.944
## 4.3333 3796 5 0.935 0.003862 0.928 0.943
## 4.4167 3791 4 0.934 0.003890 0.927 0.942
## 4.5000 3787 4 0.933 0.003917 0.926 0.941
## 4.5833 3783 5 0.932 0.003950 0.924 0.940
## 4.6667 3778 7 0.930 0.003997 0.923 0.938
## 4.7500 3771 5 0.929 0.004029 0.921 0.937
## 4.8333 3766 7 0.927 0.004074 0.920 0.935
## 4.9167 3759 9 0.925 0.004131 0.917 0.933
## 5.0000 3750 4 0.924 0.004156 0.916 0.932
## 5.0833 3746 6 0.923 0.004193 0.915 0.931
## 5.1667 3740 7 0.921 0.004236 0.913 0.929
## 5.2500 3733 5 0.920 0.004266 0.911 0.928
## 5.3333 3728 5 0.919 0.004296 0.910 0.927
## 5.4167 3723 7 0.917 0.004337 0.908 0.925
## 5.5000 3716 8 0.915 0.004383 0.906 0.924
## 5.5833 3708 6 0.913 0.004418 0.905 0.922
## 5.6667 3702 9 0.911 0.004469 0.902 0.920
## 5.7500 3693 9 0.909 0.004519 0.900 0.918
## 5.8333 3684 9 0.907 0.004568 0.898 0.916
## 5.9167 3675 8 0.905 0.004611 0.896 0.914
## 6.0000 3667 10 0.902 0.004664 0.893 0.911
## 6.0833 3657 6 0.901 0.004695 0.892 0.910
## 6.1667 3651 4 0.900 0.004716 0.891 0.909
## 6.2500 3647 4 0.899 0.004736 0.890 0.908
## 6.3333 3643 6 0.897 0.004767 0.888 0.907
## 6.4167 3637 6 0.896 0.004797 0.887 0.905
## 6.5000 3631 4 0.895 0.004817 0.886 0.904
## 6.5833 3627 6 0.893 0.004847 0.884 0.903
## 6.6667 3621 10 0.891 0.004896 0.881 0.901
## 6.7500 3611 5 0.890 0.004920 0.880 0.899
## 6.8333 3606 9 0.887 0.004963 0.878 0.897
## 6.9167 3597 7 0.886 0.004997 0.876 0.896
## 7.0000 3590 4 0.885 0.005015 0.875 0.895
## 7.0833 3586 7 0.883 0.005048 0.873 0.893
## 7.1667 3579 7 0.881 0.005080 0.871 0.891
## 7.2500 3572 3 0.881 0.005094 0.871 0.891
## 7.3333 3569 7 0.879 0.005125 0.869 0.889
## 7.4167 3562 1 0.879 0.005130 0.869 0.889
## 7.5000 3561 11 0.876 0.005179 0.866 0.886
## 7.5833 3550 8 0.874 0.005214 0.864 0.884
## 7.6667 3542 9 0.872 0.005253 0.861 0.882
## 7.7500 3533 7 0.870 0.005283 0.860 0.880
## 7.8333 3526 6 0.868 0.005308 0.858 0.879
## 7.9167 3520 10 0.866 0.005350 0.856 0.877
## 8.0000 3510 7 0.864 0.005379 0.854 0.875
## 8.0833 3503 3 0.864 0.005392 0.853 0.874
## 8.1667 3500 8 0.862 0.005424 0.851 0.872
## 8.2500 3492 5 0.860 0.005445 0.850 0.871
## 8.3333 3487 5 0.859 0.005465 0.848 0.870
## 8.4167 3482 9 0.857 0.005500 0.846 0.868
## 8.5000 3473 7 0.855 0.005528 0.844 0.866
## 8.5833 3466 9 0.853 0.005563 0.842 0.864
## 8.6667 3457 6 0.851 0.005586 0.841 0.862
## 8.7500 3451 4 0.850 0.005601 0.840 0.862
## 8.8333 3447 4 0.849 0.005617 0.839 0.861
## 8.9167 3443 8 0.848 0.005647 0.837 0.859
## 9.0000 3435 6 0.846 0.005669 0.835 0.857
## 9.0833 3429 3 0.845 0.005680 0.834 0.857
## 9.1667 3426 10 0.843 0.005717 0.832 0.854
## 9.2500 3416 2 0.842 0.005724 0.831 0.854
## 9.3333 3414 4 0.841 0.005739 0.830 0.853
## 9.4167 3410 4 0.840 0.005753 0.829 0.852
## 9.5000 3406 4 0.839 0.005768 0.828 0.851
## 9.5833 3402 8 0.837 0.005796 0.826 0.849
## 9.6667 3394 8 0.835 0.005824 0.824 0.847
## 9.7500 3386 4 0.834 0.005838 0.823 0.846
## 9.8333 3382 10 0.832 0.005873 0.821 0.844
## 9.9167 3372 5 0.831 0.005890 0.819 0.842
## 10.0000 3367 8 0.829 0.005917 0.817 0.840
## 10.0833 3359 8 0.827 0.005944 0.815 0.839
## 10.1667 3351 11 0.824 0.005981 0.812 0.836
## 10.2500 3340 3 0.823 0.005991 0.812 0.835
## 10.3333 3337 10 0.821 0.006023 0.809 0.833
## 10.4167 3327 8 0.819 0.006049 0.807 0.831
## 10.5000 3319 6 0.817 0.006068 0.806 0.829
## 10.5833 3313 4 0.816 0.006081 0.805 0.828
## 10.6667 3309 7 0.815 0.006103 0.803 0.827
## 10.7500 3302 8 0.813 0.006128 0.801 0.825
## 10.8333 3294 5 0.811 0.006143 0.800 0.824
## 10.9167 3289 8 0.810 0.006168 0.798 0.822
## 11.0000 3281 6 0.808 0.006186 0.796 0.820
## 11.0833 3275 5 0.807 0.006201 0.795 0.819
## 11.1667 3270 6 0.805 0.006219 0.793 0.818
## 11.2500 3264 5 0.804 0.006234 0.792 0.816
## 11.3333 3259 12 0.801 0.006270 0.789 0.814
## 11.4167 3247 5 0.800 0.006284 0.788 0.812
## 11.5000 3242 10 0.797 0.006313 0.785 0.810
## 11.5833 3232 9 0.795 0.006339 0.783 0.808
## 11.6667 3223 9 0.793 0.006364 0.781 0.806
## 11.7500 3214 5 0.792 0.006378 0.779 0.804
## 11.8333 3209 5 0.791 0.006392 0.778 0.803
## 11.9167 3204 7 0.789 0.006411 0.776 0.801
## 12.0000 3197 6 0.787 0.006428 0.775 0.800
## 12.0833 3191 8 0.785 0.006449 0.773 0.798
## 12.1667 3183 7 0.784 0.006468 0.771 0.796
## 12.2500 3176 3 0.783 0.006476 0.770 0.796
## 12.3333 3173 9 0.781 0.006500 0.768 0.794
## 12.4167 3164 7 0.779 0.006518 0.766 0.792
## 12.5000 3157 9 0.777 0.006541 0.764 0.790
## 12.5833 3148 9 0.774 0.006565 0.762 0.787
## 12.6667 3139 3 0.774 0.006572 0.761 0.787
## 12.7500 3136 8 0.772 0.006592 0.759 0.785
## 12.8333 3128 6 0.770 0.006607 0.757 0.783
## 12.9167 3032 6 0.769 0.006624 0.756 0.782
## 13.0000 2965 9 0.766 0.006649 0.754 0.780
## 13.0833 2935 5 0.765 0.006663 0.752 0.778
## 13.1667 2849 4 0.764 0.006675 0.751 0.777
## 13.2500 2773 4 0.763 0.006689 0.750 0.776
## 13.3333 2688 6 0.761 0.006710 0.748 0.775
## 13.4167 2583 4 0.760 0.006725 0.747 0.773
## 13.5000 2578 6 0.758 0.006748 0.745 0.772
## 13.5833 2503 3 0.757 0.006760 0.744 0.771
## 13.6667 2438 7 0.755 0.006791 0.742 0.769
## 13.7500 2342 5 0.754 0.006814 0.740 0.767
## 13.8333 2194 5 0.752 0.006842 0.739 0.765
## 13.9167 2115 3 0.751 0.006860 0.737 0.764
## 14.0000 2062 1 0.750 0.006866 0.737 0.764
## 14.0833 2048 2 0.750 0.006879 0.736 0.763
## 14.1667 2009 1 0.749 0.006886 0.736 0.763
## 14.2500 1965 6 0.747 0.006928 0.734 0.761
## 14.3333 1926 6 0.745 0.006971 0.731 0.759
## 14.4167 1857 2 0.744 0.006987 0.730 0.758
## 14.5000 1806 6 0.741 0.007036 0.728 0.755
## 14.5833 1700 2 0.741 0.007055 0.727 0.755
## 14.6667 1617 4 0.739 0.007096 0.725 0.753
## 14.7500 1518 2 0.738 0.007120 0.724 0.752
## 14.8333 1456 2 0.737 0.007147 0.723 0.751
## 14.9167 1425 1 0.736 0.007160 0.722 0.750
## 15.0000 1397 4 0.734 0.007217 0.720 0.748
## 15.0833 1340 3 0.733 0.007263 0.718 0.747
## 15.1667 1291 2 0.731 0.007296 0.717 0.746
## 15.2500 1262 1 0.731 0.007313 0.717 0.745
## 15.3333 1228 5 0.728 0.007403 0.713 0.742
## 15.4167 1178 3 0.726 0.007461 0.711 0.741
## 15.5000 1145 2 0.725 0.007502 0.710 0.740
## 15.5833 1114 2 0.723 0.007545 0.709 0.738
## 15.6667 1079 2 0.722 0.007590 0.707 0.737
## 15.7500 1018 1 0.721 0.007616 0.707 0.736
## 15.8333 860 1 0.721 0.007653 0.706 0.736
## 15.9167 803 3 0.718 0.007781 0.703 0.733
## 16.0000 770 2 0.716 0.007871 0.701 0.732
## 16.0833 670 1 0.715 0.007932 0.699 0.731
## 16.2500 548 1 0.714 0.008024 0.698 0.729
## 16.3333 512 3 0.709 0.008332 0.693 0.726
## 16.5833 220 1 0.706 0.008896 0.689 0.724
##
## BOOZE_q=0–0.5/week
## time n.risk n.event survival std.err lower 95% CI upper 95% CI
## 0.0000 941 1 0.999 0.00106 0.997 1.000
## 0.0833 940 2 0.997 0.00184 0.993 1.000
## 0.2500 938 1 0.996 0.00212 0.992 1.000
## 0.3333 937 1 0.995 0.00237 0.990 0.999
## 0.5000 936 1 0.994 0.00259 0.989 0.999
## 0.5833 935 2 0.991 0.00299 0.986 0.997
## 0.6667 933 1 0.990 0.00317 0.984 0.997
## 0.8333 932 1 0.989 0.00334 0.983 0.996
## 0.9167 931 1 0.988 0.00350 0.981 0.995
## 1.1667 930 2 0.986 0.00381 0.979 0.994
## 1.2500 928 1 0.985 0.00395 0.977 0.993
## 1.3333 927 1 0.984 0.00408 0.976 0.992
## 1.4167 926 1 0.983 0.00421 0.975 0.991
## 1.5000 925 2 0.981 0.00447 0.972 0.990
## 1.7500 923 2 0.979 0.00470 0.970 0.988
## 1.8333 921 1 0.978 0.00482 0.968 0.987
## 2.0000 920 1 0.977 0.00493 0.967 0.986
## 2.0833 919 1 0.976 0.00503 0.966 0.985
## 2.1667 918 1 0.974 0.00514 0.964 0.985
## 2.2500 917 2 0.972 0.00534 0.962 0.983
## 2.3333 915 1 0.971 0.00544 0.961 0.982
## 2.4167 914 1 0.970 0.00554 0.959 0.981
## 2.5000 913 1 0.969 0.00563 0.958 0.980
## 2.5833 912 1 0.968 0.00573 0.957 0.979
## 2.6667 911 2 0.966 0.00591 0.954 0.978
## 2.7500 909 2 0.964 0.00608 0.952 0.976
## 2.8333 907 2 0.962 0.00625 0.950 0.974
## 2.9167 905 1 0.961 0.00634 0.948 0.973
## 3.0000 904 1 0.960 0.00642 0.947 0.972
## 3.0833 903 1 0.959 0.00650 0.946 0.971
## 3.2500 902 3 0.955 0.00673 0.942 0.969
## 3.3333 899 2 0.953 0.00688 0.940 0.967
## 3.5000 897 1 0.952 0.00696 0.939 0.966
## 3.6667 896 2 0.950 0.00710 0.936 0.964
## 3.7500 894 3 0.947 0.00731 0.933 0.961
## 3.8333 891 1 0.946 0.00738 0.931 0.960
## 4.0000 890 1 0.945 0.00745 0.930 0.959
## 4.0833 889 1 0.944 0.00752 0.929 0.959
## 4.4167 888 1 0.943 0.00758 0.928 0.958
## 4.5000 887 1 0.942 0.00765 0.927 0.957
## 4.7500 886 3 0.938 0.00784 0.923 0.954
## 4.9167 883 1 0.937 0.00790 0.922 0.953
## 5.0000 882 1 0.936 0.00796 0.921 0.952
## 5.0833 881 2 0.934 0.00809 0.918 0.950
## 5.3333 879 2 0.932 0.00821 0.916 0.948
## 5.4167 877 1 0.931 0.00827 0.915 0.947
## 5.5833 876 1 0.930 0.00833 0.914 0.946
## 5.6667 875 1 0.929 0.00838 0.913 0.945
## 5.7500 874 2 0.927 0.00850 0.910 0.943
## 5.8333 872 1 0.926 0.00855 0.909 0.943
## 5.9167 871 1 0.925 0.00861 0.908 0.942
## 6.1667 870 4 0.920 0.00883 0.903 0.938
## 6.2500 866 1 0.919 0.00888 0.902 0.937
## 6.3333 865 2 0.917 0.00899 0.900 0.935
## 6.5000 863 1 0.916 0.00904 0.898 0.934
## 6.5833 862 3 0.913 0.00919 0.895 0.931
## 6.6667 859 1 0.912 0.00924 0.894 0.930
## 7.0833 858 1 0.911 0.00929 0.893 0.929
## 7.1667 857 1 0.910 0.00934 0.892 0.928
## 7.2500 856 1 0.909 0.00939 0.890 0.927
## 7.3333 855 2 0.906 0.00949 0.888 0.925
## 7.5000 853 2 0.904 0.00959 0.886 0.923
## 7.5833 851 2 0.902 0.00968 0.883 0.921
## 7.7500 849 1 0.901 0.00973 0.882 0.920
## 7.8333 848 1 0.900 0.00978 0.881 0.919
## 8.0000 847 2 0.898 0.00987 0.879 0.918
## 8.0833 845 2 0.896 0.00996 0.877 0.916
## 8.1667 843 2 0.894 0.01005 0.874 0.914
## 8.3333 841 1 0.893 0.01009 0.873 0.913
## 8.4167 840 1 0.892 0.01013 0.872 0.912
## 8.5000 839 2 0.889 0.01022 0.870 0.910
## 8.5833 837 1 0.888 0.01026 0.869 0.909
## 8.6667 836 3 0.885 0.01039 0.865 0.906
## 8.7500 833 2 0.883 0.01047 0.863 0.904
## 8.8333 831 2 0.881 0.01056 0.861 0.902
## 8.9167 829 1 0.880 0.01060 0.859 0.901
## 9.0000 828 3 0.877 0.01072 0.856 0.898
## 9.0833 825 1 0.876 0.01076 0.855 0.897
## 9.3333 824 1 0.875 0.01080 0.854 0.896
## 9.4167 823 1 0.874 0.01083 0.853 0.895
## 9.5833 822 2 0.871 0.01091 0.850 0.893
## 9.6667 820 3 0.868 0.01103 0.847 0.890
## 9.7500 817 3 0.865 0.01114 0.843 0.887
## 9.8333 814 1 0.864 0.01118 0.842 0.886
## 10.0833 813 1 0.863 0.01121 0.841 0.885
## 10.2500 812 4 0.859 0.01136 0.837 0.881
## 10.5833 808 2 0.857 0.01143 0.834 0.879
## 10.6667 806 2 0.854 0.01150 0.832 0.877
## 10.7500 804 2 0.852 0.01157 0.830 0.875
## 10.9167 802 3 0.849 0.01167 0.827 0.872
## 11.0000 799 2 0.847 0.01174 0.824 0.870
## 11.0833 797 1 0.846 0.01177 0.823 0.869
## 11.1667 796 1 0.845 0.01180 0.822 0.868
## 11.2500 795 2 0.843 0.01187 0.820 0.866
## 11.3333 793 1 0.842 0.01190 0.819 0.865
## 11.4167 792 2 0.840 0.01197 0.816 0.863
## 11.5000 790 2 0.837 0.01203 0.814 0.861
## 11.5833 788 3 0.834 0.01212 0.811 0.858
## 11.6667 785 1 0.833 0.01215 0.810 0.857
## 11.7500 784 1 0.832 0.01218 0.809 0.856
## 11.8333 783 1 0.831 0.01222 0.807 0.855
## 11.9167 782 2 0.829 0.01228 0.805 0.853
## 12.0000 780 1 0.828 0.01231 0.804 0.852
## 12.0833 779 1 0.827 0.01234 0.803 0.851
## 12.1667 778 3 0.824 0.01243 0.800 0.848
## 12.2500 775 2 0.821 0.01248 0.797 0.846
## 12.3333 773 1 0.820 0.01251 0.796 0.845
## 12.4167 772 1 0.819 0.01254 0.795 0.844
## 12.5000 771 1 0.818 0.01257 0.794 0.843
## 12.6667 770 3 0.815 0.01266 0.791 0.840
## 12.7500 767 1 0.814 0.01268 0.790 0.839
## 12.8333 766 1 0.813 0.01271 0.788 0.838
## 13.0833 738 1 0.812 0.01274 0.787 0.837
## 13.1667 718 3 0.808 0.01284 0.784 0.834
## 13.2500 697 1 0.807 0.01287 0.782 0.833
## 13.3333 668 2 0.805 0.01295 0.780 0.831
## 13.4167 642 2 0.802 0.01303 0.777 0.828
## 13.5000 639 1 0.801 0.01307 0.776 0.827
## 13.5833 613 2 0.799 0.01315 0.773 0.825
## 13.6667 591 3 0.794 0.01329 0.769 0.821
## 13.8333 556 2 0.792 0.01340 0.766 0.818
## 14.0000 522 1 0.790 0.01346 0.764 0.817
## 14.0833 515 2 0.787 0.01358 0.761 0.814
## 14.2500 482 5 0.779 0.01392 0.752 0.807
## 14.3333 471 1 0.777 0.01399 0.750 0.805
## 14.4167 442 1 0.775 0.01407 0.748 0.804
## 14.6667 390 2 0.771 0.01427 0.744 0.800
## 15.0000 329 1 0.769 0.01442 0.741 0.798
## 15.2500 283 1 0.766 0.01463 0.738 0.796
## 15.6667 219 1 0.763 0.01497 0.734 0.793
## 15.7500 197 1 0.759 0.01539 0.729 0.790
## 15.9167 173 1 0.755 0.01591 0.724 0.786
## 16.1667 141 1 0.749 0.01668 0.717 0.783
## 16.5833 57 1 0.736 0.02093 0.696 0.778
## 16.6667 45 1 0.720 0.02609 0.670 0.773
##
## BOOZE_q=0.5–2/week
## time n.risk n.event survival std.err lower 95% CI upper 95% CI
## 0.0000 1729 1 0.999 0.000578 0.998 1.000
## 0.0833 1728 1 0.999 0.000817 0.997 1.000
## 0.1667 1727 1 0.998 0.001001 0.996 1.000
## 0.3333 1726 1 0.998 0.001155 0.995 1.000
## 0.4167 1725 1 0.997 0.001291 0.995 1.000
## 0.7500 1724 1 0.997 0.001414 0.994 0.999
## 0.8333 1723 1 0.996 0.001527 0.993 0.999
## 1.0000 1722 3 0.994 0.001824 0.991 0.998
## 1.1667 1719 2 0.993 0.001997 0.989 0.997
## 1.2500 1717 3 0.991 0.002230 0.987 0.996
## 1.3333 1714 2 0.990 0.002373 0.986 0.995
## 1.4167 1712 1 0.990 0.002441 0.985 0.994
## 1.5000 1711 1 0.989 0.002507 0.984 0.994
## 1.5833 1710 1 0.988 0.002572 0.983 0.993
## 1.6667 1709 2 0.987 0.002695 0.982 0.993
## 1.7500 1707 2 0.986 0.002814 0.981 0.992
## 1.8333 1705 1 0.986 0.002871 0.980 0.991
## 2.0000 1704 1 0.985 0.002927 0.979 0.991
## 2.0833 1703 2 0.984 0.003036 0.978 0.990
## 2.1667 1701 1 0.983 0.003088 0.977 0.989
## 2.2500 1700 1 0.983 0.003140 0.977 0.989
## 2.3333 1699 1 0.982 0.003191 0.976 0.988
## 2.5000 1698 4 0.980 0.003387 0.973 0.986
## 2.5833 1694 2 0.979 0.003480 0.972 0.985
## 2.6667 1692 2 0.977 0.003571 0.970 0.984
## 2.7500 1690 3 0.976 0.003702 0.968 0.983
## 2.8333 1687 3 0.974 0.003829 0.966 0.982
## 2.9167 1684 4 0.972 0.003991 0.964 0.980
## 3.0000 1680 1 0.971 0.004030 0.963 0.979
## 3.0833 1679 2 0.970 0.004107 0.962 0.978
## 3.1667 1677 2 0.969 0.004183 0.961 0.977
## 3.2500 1675 1 0.968 0.004221 0.960 0.976
## 3.3333 1674 2 0.967 0.004294 0.959 0.975
## 3.4167 1672 2 0.966 0.004366 0.957 0.974
## 3.5000 1670 1 0.965 0.004402 0.957 0.974
## 3.5833 1669 2 0.964 0.004472 0.955 0.973
## 3.6667 1667 3 0.962 0.004574 0.953 0.971
## 3.7500 1664 1 0.962 0.004608 0.953 0.971
## 3.8333 1663 3 0.960 0.004707 0.951 0.969
## 4.0000 1660 5 0.957 0.004868 0.948 0.967
## 4.0833 1655 3 0.955 0.004961 0.946 0.965
## 4.1667 1652 4 0.953 0.005082 0.943 0.963
## 4.2500 1648 1 0.953 0.005112 0.943 0.963
## 4.3333 1647 2 0.951 0.005170 0.941 0.962
## 4.4167 1645 1 0.951 0.005200 0.941 0.961
## 4.5000 1644 1 0.950 0.005228 0.940 0.961
## 4.5833 1643 1 0.950 0.005257 0.939 0.960
## 4.6667 1642 2 0.949 0.005314 0.938 0.959
## 4.7500 1640 2 0.947 0.005370 0.937 0.958
## 4.8333 1638 1 0.947 0.005398 0.936 0.957
## 4.9167 1637 2 0.946 0.005453 0.935 0.956
## 5.0000 1635 2 0.944 0.005507 0.934 0.955
## 5.0833 1633 4 0.942 0.005614 0.931 0.953
## 5.2500 1629 2 0.941 0.005666 0.930 0.952
## 5.4167 1627 2 0.940 0.005718 0.929 0.951
## 5.5000 1625 1 0.939 0.005744 0.928 0.951
## 5.5833 1624 1 0.939 0.005769 0.927 0.950
## 5.6667 1623 1 0.938 0.005795 0.927 0.950
## 5.7500 1622 2 0.937 0.005845 0.926 0.948
## 5.8333 1620 1 0.936 0.005870 0.925 0.948
## 6.0833 1619 2 0.935 0.005919 0.924 0.947
## 6.1667 1617 1 0.935 0.005944 0.923 0.946
## 6.2500 1616 1 0.934 0.005968 0.922 0.946
## 6.4167 1615 3 0.932 0.006041 0.921 0.944
## 6.5000 1612 1 0.932 0.006065 0.920 0.944
## 6.5833 1611 4 0.929 0.006159 0.917 0.942
## 6.6667 1607 3 0.928 0.006228 0.916 0.940
## 6.7500 1604 4 0.925 0.006319 0.913 0.938
## 6.8333 1600 1 0.925 0.006342 0.912 0.937
## 6.9167 1599 1 0.924 0.006364 0.912 0.937
## 7.0000 1598 1 0.924 0.006386 0.911 0.936
## 7.0833 1597 1 0.923 0.006408 0.911 0.936
## 7.1667 1596 1 0.922 0.006430 0.910 0.935
## 7.2500 1595 1 0.922 0.006452 0.909 0.935
## 7.3333 1594 4 0.920 0.006539 0.907 0.933
## 7.4167 1590 1 0.919 0.006560 0.906 0.932
## 7.5000 1589 1 0.918 0.006582 0.906 0.931
## 7.5833 1588 5 0.916 0.006687 0.903 0.929
## 7.6667 1583 6 0.912 0.006810 0.899 0.926
## 7.7500 1577 1 0.912 0.006830 0.898 0.925
## 7.8333 1576 1 0.911 0.006850 0.898 0.924
## 7.9167 1575 1 0.910 0.006870 0.897 0.924
## 8.0000 1574 3 0.909 0.006930 0.895 0.922
## 8.0833 1571 3 0.907 0.006989 0.893 0.921
## 8.1667 1568 4 0.905 0.007066 0.891 0.919
## 8.2500 1564 3 0.903 0.007123 0.889 0.917
## 8.3333 1561 3 0.901 0.007179 0.887 0.915
## 8.4167 1558 2 0.900 0.007217 0.886 0.914
## 8.5000 1556 3 0.898 0.007272 0.884 0.913
## 8.5833 1553 1 0.898 0.007290 0.883 0.912
## 8.6667 1552 1 0.897 0.007308 0.883 0.911
## 8.7500 1551 3 0.895 0.007363 0.881 0.910
## 8.8333 1548 1 0.895 0.007381 0.880 0.909
## 8.9167 1547 2 0.894 0.007416 0.879 0.908
## 9.0000 1545 1 0.893 0.007434 0.879 0.908
## 9.1667 1544 3 0.891 0.007487 0.877 0.906
## 9.4167 1541 1 0.891 0.007504 0.876 0.906
## 9.5000 1540 1 0.890 0.007521 0.875 0.905
## 9.5833 1539 3 0.888 0.007573 0.874 0.903
## 9.6667 1536 1 0.888 0.007590 0.873 0.903
## 9.7500 1535 2 0.887 0.007624 0.872 0.902
## 9.8333 1533 2 0.885 0.007658 0.871 0.901
## 9.9167 1531 2 0.884 0.007692 0.869 0.900
## 10.0000 1529 1 0.884 0.007708 0.869 0.899
## 10.0833 1528 3 0.882 0.007758 0.867 0.897
## 10.1667 1525 3 0.880 0.007807 0.865 0.896
## 10.2500 1522 2 0.879 0.007840 0.864 0.895
## 10.3333 1520 1 0.879 0.007856 0.863 0.894
## 10.4167 1519 4 0.876 0.007920 0.861 0.892
## 10.5000 1515 3 0.874 0.007967 0.859 0.890
## 10.5833 1512 5 0.872 0.008045 0.856 0.888
## 10.6667 1507 1 0.871 0.008061 0.855 0.887
## 10.7500 1506 1 0.870 0.008076 0.855 0.886
## 10.8333 1505 2 0.869 0.008107 0.854 0.885
## 10.9167 1503 2 0.868 0.008137 0.852 0.884
## 11.0000 1501 2 0.867 0.008167 0.851 0.883
## 11.1667 1499 1 0.866 0.008182 0.851 0.883
## 11.2500 1498 2 0.865 0.008212 0.849 0.881
## 11.3333 1496 2 0.864 0.008242 0.848 0.880
## 11.4167 1494 1 0.864 0.008256 0.847 0.880
## 11.5000 1493 3 0.862 0.008300 0.846 0.878
## 11.5833 1490 3 0.860 0.008344 0.844 0.877
## 11.6667 1487 2 0.859 0.008373 0.843 0.875
## 11.7500 1485 2 0.858 0.008401 0.841 0.874
## 11.8333 1483 3 0.856 0.008444 0.840 0.873
## 11.9167 1480 2 0.855 0.008472 0.838 0.872
## 12.0000 1478 2 0.854 0.008500 0.837 0.870
## 12.1667 1476 4 0.851 0.008555 0.835 0.868
## 12.2500 1472 3 0.850 0.008596 0.833 0.867
## 12.3333 1469 3 0.848 0.008637 0.831 0.865
## 12.4167 1466 3 0.846 0.008677 0.829 0.863
## 12.5000 1463 1 0.846 0.008690 0.829 0.863
## 12.6667 1462 2 0.844 0.008717 0.828 0.862
## 12.7500 1460 5 0.842 0.008782 0.824 0.859
## 12.8333 1455 2 0.840 0.008808 0.823 0.858
## 12.9167 1425 3 0.839 0.008849 0.821 0.856
## 13.0000 1403 1 0.838 0.008863 0.821 0.856
## 13.0833 1393 1 0.837 0.008877 0.820 0.855
## 13.1667 1364 4 0.835 0.008935 0.818 0.853
## 13.2500 1330 4 0.832 0.008996 0.815 0.850
## 13.3333 1287 2 0.831 0.009029 0.814 0.849
## 13.5000 1230 1 0.830 0.009046 0.813 0.848
## 13.5833 1182 1 0.830 0.009066 0.812 0.848
## 13.6667 1156 2 0.828 0.009107 0.811 0.846
## 13.7500 1119 2 0.827 0.009151 0.809 0.845
## 13.8333 1086 1 0.826 0.009174 0.808 0.844
## 13.9167 1071 1 0.825 0.009198 0.807 0.844
## 14.0833 1037 1 0.825 0.009223 0.807 0.843
## 14.1667 999 2 0.823 0.009278 0.805 0.841
## 14.2500 955 1 0.822 0.009308 0.804 0.840
## 14.3333 931 3 0.819 0.009403 0.801 0.838
## 14.4167 879 2 0.817 0.009474 0.799 0.836
## 14.5000 815 1 0.816 0.009515 0.798 0.835
## 14.9167 676 2 0.814 0.009639 0.795 0.833
## 15.0000 660 2 0.812 0.009766 0.793 0.831
## 15.0833 647 2 0.809 0.009896 0.790 0.829
## 15.1667 628 3 0.805 0.010097 0.786 0.825
## 15.5000 498 3 0.800 0.010418 0.780 0.821
## 15.5833 463 3 0.795 0.010772 0.774 0.817
## 15.9167 340 1 0.793 0.010991 0.772 0.815
##
## BOOZE_q=>2/week
## time n.risk n.event survival std.err lower 95% CI upper 95% CI
## 0.167 2527 4 0.998 0.000791 0.997 1.000
## 0.250 2523 1 0.998 0.000884 0.996 1.000
## 0.333 2522 4 0.996 0.001185 0.994 0.999
## 0.417 2518 1 0.996 0.001249 0.994 0.998
## 0.500 2517 4 0.994 0.001477 0.992 0.997
## 0.583 2513 1 0.994 0.001528 0.991 0.997
## 0.750 2512 1 0.994 0.001578 0.991 0.997
## 0.833 2511 3 0.992 0.001718 0.989 0.996
## 0.917 2508 1 0.992 0.001763 0.989 0.996
## 1.000 2507 2 0.991 0.001848 0.988 0.995
## 1.083 2505 4 0.990 0.002007 0.986 0.994
## 1.167 2501 2 0.989 0.002082 0.985 0.993
## 1.250 2499 1 0.989 0.002119 0.984 0.993
## 1.333 2498 3 0.987 0.002224 0.983 0.992
## 1.417 2495 1 0.987 0.002258 0.983 0.991
## 1.500 2494 3 0.986 0.002357 0.981 0.990
## 1.583 2491 1 0.985 0.002389 0.981 0.990
## 1.750 2490 1 0.985 0.002421 0.980 0.990
## 1.833 2489 2 0.984 0.002483 0.979 0.989
## 2.000 2487 1 0.984 0.002513 0.979 0.989
## 2.083 2486 1 0.983 0.002543 0.978 0.988
## 2.167 2485 1 0.983 0.002573 0.978 0.988
## 2.250 2484 1 0.983 0.002602 0.978 0.988
## 2.333 2483 1 0.982 0.002631 0.977 0.987
## 2.417 2482 1 0.982 0.002659 0.977 0.987
## 2.500 2481 4 0.980 0.002770 0.975 0.986
## 2.583 2477 2 0.979 0.002824 0.974 0.985
## 2.667 2475 1 0.979 0.002851 0.973 0.985
## 2.833 2474 3 0.978 0.002928 0.972 0.984
## 2.917 2471 3 0.977 0.003004 0.971 0.983
## 3.000 2468 6 0.974 0.003149 0.968 0.980
## 3.083 2462 3 0.973 0.003219 0.967 0.979
## 3.167 2459 1 0.973 0.003242 0.966 0.979
## 3.250 2458 3 0.972 0.003310 0.965 0.978
## 3.333 2455 2 0.971 0.003354 0.964 0.977
## 3.417 2453 1 0.970 0.003376 0.964 0.977
## 3.500 2452 3 0.969 0.003441 0.962 0.976
## 3.583 2449 3 0.968 0.003504 0.961 0.975
## 3.667 2446 2 0.967 0.003546 0.960 0.974
## 3.750 2444 1 0.967 0.003566 0.960 0.974
## 3.833 2443 3 0.966 0.003627 0.958 0.973
## 3.917 2440 2 0.965 0.003667 0.958 0.972
## 4.000 2438 4 0.963 0.003745 0.956 0.971
## 4.250 2434 1 0.963 0.003765 0.955 0.970
## 4.333 2433 1 0.962 0.003784 0.955 0.970
## 4.417 2432 2 0.962 0.003822 0.954 0.969
## 4.500 2430 3 0.960 0.003878 0.953 0.968
## 4.583 2427 2 0.960 0.003915 0.952 0.967
## 4.667 2425 4 0.958 0.003988 0.950 0.966
## 4.750 2421 4 0.956 0.004059 0.949 0.964
## 4.833 2417 3 0.955 0.004111 0.947 0.963
## 4.917 2414 2 0.954 0.004146 0.946 0.963
## 5.000 2412 4 0.953 0.004214 0.945 0.961
## 5.083 2408 6 0.951 0.004314 0.942 0.959
## 5.167 2402 1 0.950 0.004330 0.942 0.959
## 5.250 2401 4 0.949 0.004394 0.940 0.957
## 5.333 2397 1 0.948 0.004410 0.940 0.957
## 5.417 2396 4 0.947 0.004473 0.938 0.955
## 5.500 2392 2 0.946 0.004505 0.937 0.955
## 5.583 2390 3 0.945 0.004551 0.936 0.954
## 5.667 2387 3 0.943 0.004596 0.934 0.952
## 5.750 2384 7 0.941 0.004701 0.931 0.950
## 5.833 2377 5 0.939 0.004773 0.929 0.948
## 5.917 2372 4 0.937 0.004830 0.928 0.947
## 6.000 2368 1 0.937 0.004845 0.927 0.946
## 6.167 2367 2 0.936 0.004873 0.926 0.945
## 6.250 2365 2 0.935 0.004901 0.926 0.945
## 6.333 2363 5 0.933 0.004969 0.923 0.943
## 6.417 2358 2 0.932 0.004997 0.923 0.942
## 6.583 2356 3 0.931 0.005037 0.921 0.941
## 6.667 2353 8 0.928 0.005143 0.918 0.938
## 6.750 2345 2 0.927 0.005169 0.917 0.937
## 6.833 2343 1 0.927 0.005182 0.917 0.937
## 6.917 2342 1 0.926 0.005195 0.916 0.937
## 7.000 2341 4 0.925 0.005246 0.915 0.935
## 7.083 2337 2 0.924 0.005271 0.914 0.934
## 7.167 2335 5 0.922 0.005333 0.912 0.933
## 7.250 2330 4 0.920 0.005383 0.910 0.931
## 7.333 2326 4 0.919 0.005431 0.908 0.930
## 7.417 2322 3 0.918 0.005467 0.907 0.928
## 7.500 2319 3 0.917 0.005503 0.906 0.927
## 7.583 2316 7 0.914 0.005585 0.903 0.925
## 7.667 2309 7 0.911 0.005665 0.900 0.922
## 7.750 2302 2 0.910 0.005688 0.899 0.921
## 7.833 2300 3 0.909 0.005722 0.898 0.920
## 8.000 2297 3 0.908 0.005755 0.897 0.919
## 8.083 2294 3 0.907 0.005788 0.895 0.918
## 8.167 2291 2 0.906 0.005810 0.895 0.917
## 8.250 2289 1 0.905 0.005821 0.894 0.917
## 8.333 2288 4 0.904 0.005865 0.892 0.915
## 8.417 2284 4 0.902 0.005908 0.891 0.914
## 8.500 2280 7 0.899 0.005981 0.888 0.911
## 8.583 2273 2 0.899 0.006002 0.887 0.911
## 8.667 2271 5 0.897 0.006054 0.885 0.909
## 8.750 2266 4 0.895 0.006095 0.883 0.907
## 8.833 2262 6 0.893 0.006155 0.881 0.905
## 8.917 2256 5 0.891 0.006205 0.879 0.903
## 9.000 2251 5 0.889 0.006254 0.877 0.901
## 9.083 2246 4 0.887 0.006293 0.875 0.900
## 9.167 2242 4 0.886 0.006331 0.873 0.898
## 9.250 2238 2 0.885 0.006350 0.872 0.897
## 9.333 2236 2 0.884 0.006369 0.872 0.897
## 9.417 2234 4 0.882 0.006407 0.870 0.895
## 9.500 2230 4 0.881 0.006444 0.868 0.894
## 9.583 2226 6 0.879 0.006499 0.866 0.891
## 9.667 2220 3 0.877 0.006526 0.865 0.890
## 9.750 2217 3 0.876 0.006553 0.863 0.889
## 9.833 2214 3 0.875 0.006580 0.862 0.888
## 9.917 2211 1 0.875 0.006589 0.862 0.888
## 10.000 2210 2 0.874 0.006607 0.861 0.887
## 10.083 2208 5 0.872 0.006651 0.859 0.885
## 10.167 2203 5 0.870 0.006694 0.857 0.883
## 10.250 2198 2 0.869 0.006712 0.856 0.882
## 10.333 2196 3 0.868 0.006737 0.855 0.881
## 10.417 2193 7 0.865 0.006797 0.852 0.878
## 10.500 2186 3 0.864 0.006822 0.851 0.877
## 10.583 2183 1 0.863 0.006830 0.850 0.877
## 10.667 2182 6 0.861 0.006880 0.848 0.875
## 10.750 2176 3 0.860 0.006904 0.846 0.874
## 10.833 2173 2 0.859 0.006921 0.846 0.873
## 10.917 2171 5 0.857 0.006961 0.844 0.871
## 11.000 2166 6 0.855 0.007009 0.841 0.869
## 11.083 2160 4 0.853 0.007040 0.839 0.867
## 11.167 2156 1 0.853 0.007048 0.839 0.867
## 11.250 2155 3 0.852 0.007072 0.838 0.866
## 11.333 2152 2 0.851 0.007087 0.837 0.865
## 11.417 2150 3 0.850 0.007110 0.836 0.864
## 11.500 2147 3 0.848 0.007134 0.835 0.863
## 11.583 2144 3 0.847 0.007156 0.833 0.861
## 11.667 2141 6 0.845 0.007202 0.831 0.859
## 11.750 2135 4 0.843 0.007232 0.829 0.858
## 11.833 2131 2 0.843 0.007246 0.828 0.857
## 11.917 2129 1 0.842 0.007254 0.828 0.856
## 12.000 2128 1 0.842 0.007261 0.828 0.856
## 12.083 2127 2 0.841 0.007276 0.827 0.855
## 12.167 2125 5 0.839 0.007312 0.825 0.853
## 12.250 2120 4 0.837 0.007341 0.823 0.852
## 12.333 2116 3 0.836 0.007363 0.822 0.851
## 12.417 2113 8 0.833 0.007419 0.819 0.848
## 12.500 2105 3 0.832 0.007441 0.817 0.847
## 12.583 2102 2 0.831 0.007454 0.817 0.846
## 12.667 2100 5 0.829 0.007489 0.814 0.844
## 12.750 2095 1 0.829 0.007496 0.814 0.843
## 12.833 2094 4 0.827 0.007523 0.812 0.842
## 12.917 2060 2 0.826 0.007537 0.812 0.841
## 13.000 2045 3 0.825 0.007559 0.810 0.840
## 13.083 2032 4 0.823 0.007587 0.809 0.838
## 13.167 1988 5 0.821 0.007625 0.807 0.836
## 13.250 1948 2 0.821 0.007640 0.806 0.836
## 13.333 1886 6 0.818 0.007690 0.803 0.833
## 13.417 1804 7 0.815 0.007753 0.800 0.830
## 13.500 1794 3 0.813 0.007780 0.798 0.829
## 13.583 1720 2 0.812 0.007799 0.797 0.828
## 13.667 1681 5 0.810 0.007851 0.795 0.826
## 13.750 1646 4 0.808 0.007893 0.793 0.824
## 13.833 1612 4 0.806 0.007937 0.791 0.822
## 13.917 1589 3 0.805 0.007970 0.789 0.820
## 14.000 1565 1 0.804 0.007982 0.789 0.820
## 14.083 1538 1 0.803 0.007994 0.788 0.819
## 14.167 1464 2 0.802 0.008020 0.787 0.818
## 14.250 1414 3 0.801 0.008063 0.785 0.817
## 14.333 1387 1 0.800 0.008078 0.784 0.816
## 14.417 1328 2 0.799 0.008111 0.783 0.815
## 14.500 1260 5 0.796 0.008202 0.780 0.812
## 14.583 1220 2 0.794 0.008240 0.778 0.811
## 14.667 1188 1 0.794 0.008260 0.778 0.810
## 14.750 1115 4 0.791 0.008352 0.775 0.807
## 14.833 1041 1 0.790 0.008379 0.774 0.807
## 14.917 1001 1 0.789 0.008407 0.773 0.806
## 15.000 963 2 0.788 0.008469 0.771 0.804
## 15.333 808 2 0.786 0.008560 0.769 0.803
## 15.417 755 3 0.783 0.008714 0.766 0.800
## 15.583 614 3 0.779 0.008946 0.761 0.797
## 15.667 589 1 0.777 0.009028 0.760 0.795
## 15.750 548 1 0.776 0.009123 0.758 0.794
## 15.833 530 2 0.773 0.009320 0.755 0.792
## 15.917 492 1 0.772 0.009433 0.753 0.790
## 16.000 460 1 0.770 0.009560 0.751 0.789
## 16.083 441 1 0.768 0.009697 0.749 0.787
## 16.167 422 1 0.766 0.009843 0.747 0.786
## 16.250 362 3 0.760 0.010422 0.740 0.781
## 16.417 225 1 0.757 0.010910 0.736 0.778
## 16.500 187 2 0.749 0.012201 0.725 0.773
## 16.583 169 1 0.744 0.012908 0.719 0.770
## 16.750 82 1 0.735 0.015618 0.705 0.766
summary(fit)$table
## records n.max n.start events rmean se(rmean) median
## BOOZE_q=0/week 4053 4053 4053 1070 14.42166 0.07071389 NA
## BOOZE_q=0–0.5/week 941 941 941 213 14.83118 0.13643836 NA
## BOOZE_q=0.5–2/week 1729 1729 1729 325 15.15602 0.09331554 NA
## BOOZE_q=>2/week 2527 2527 2527 537 15.03836 0.07729027 NA
## 0.95LCL 0.95UCL
## BOOZE_q=0/week NA NA
## BOOZE_q=0–0.5/week NA NA
## BOOZE_q=0.5–2/week NA NA
## BOOZE_q=>2/week NA NA
#Log-rank test
survdiff(Surv(FU, DEATH)~BOOZE_q, data=d)
## Call:
## survdiff(formula = Surv(FU, DEATH) ~ BOOZE_q, data = d)
##
## N Observed Expected (O-E)^2/E (O-E)^2/V
## BOOZE_q=0/week 4053 1070 916 25.873 45.243
## BOOZE_q=0–0.5/week 941 213 219 0.139 0.155
## BOOZE_q=0.5–2/week 1729 325 411 18.013 22.319
## BOOZE_q=>2/week 2527 537 599 6.493 9.027
##
## Chisq= 50.6 on 3 degrees of freedom, p= 6e-11
#Sensitivity analysis (Poisson model)
#Exclude follow-up = 0
d_pois <- d %>%
filter(FU > 0)
poisson <- glm(DEATH ~ as.factor(BOOZE_q) + SEX +
as.factor(RACE) + as.factor(EDUC_CAT) +
as.factor(MARRY) + BMI + AVGSMK + SIZE,
family = poisson(link = "log"),
offset = log(FU), data = d_pois)
summary(poisson)
##
## Call:
## glm(formula = DEATH ~ as.factor(BOOZE_q) + SEX + as.factor(RACE) +
## as.factor(EDUC_CAT) + as.factor(MARRY) + BMI + AVGSMK + SIZE,
## family = poisson(link = "log"), data = d_pois, offset = log(FU))
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -2.062977 0.158501 -13.016 < 2e-16 ***
## as.factor(BOOZE_q)0–0.5/week -0.134545 0.075929 -1.772 0.076396 .
## as.factor(BOOZE_q)0.5–2/week -0.385969 0.065539 -5.889 3.88e-09 ***
## as.factor(BOOZE_q)>2/week -0.330099 0.058374 -5.655 1.56e-08 ***
## SEX -0.753542 0.048917 -15.404 < 2e-16 ***
## as.factor(RACE)2 -0.248327 0.074381 -3.339 0.000842 ***
## as.factor(RACE)3 -0.569211 0.202344 -2.813 0.004907 **
## as.factor(EDUC_CAT)Some HS -0.234804 0.061827 -3.798 0.000146 ***
## as.factor(EDUC_CAT)HS Grad -0.520908 0.056631 -9.198 < 2e-16 ***
## as.factor(EDUC_CAT)Some College -0.657512 0.082491 -7.971 1.58e-15 ***
## as.factor(EDUC_CAT)College+ -0.852910 0.088854 -9.599 < 2e-16 ***
## as.factor(MARRY)3 0.684032 0.061927 11.046 < 2e-16 ***
## as.factor(MARRY)4 0.044207 0.102176 0.433 0.665268
## as.factor(MARRY)5 0.047057 0.144526 0.326 0.744732
## as.factor(MARRY)6 0.157825 0.096299 1.639 0.101232
## as.factor(MARRY)8 0.756500 0.335993 2.252 0.024352 *
## BMI -0.013210 0.004669 -2.829 0.004663 **
## AVGSMK 0.004975 0.001627 3.058 0.002225 **
## SIZE -0.023602 0.008487 -2.781 0.005422 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for poisson family taken to be 1)
##
## Null deviance: 9335.0 on 9245 degrees of freedom
## Residual deviance: 8739.6 on 9227 degrees of freedom
## AIC: 13060
##
## Number of Fisher Scoring iterations: 6
exp(coef(poisson))
## (Intercept) as.factor(BOOZE_q)0–0.5/week
## 0.1270752 0.8741136
## as.factor(BOOZE_q)0.5–2/week as.factor(BOOZE_q)>2/week
## 0.6797914 0.7188525
## SEX as.factor(RACE)2
## 0.4706965 0.7801048
## as.factor(RACE)3 as.factor(EDUC_CAT)Some HS
## 0.5659720 0.7907257
## as.factor(EDUC_CAT)HS Grad as.factor(EDUC_CAT)Some College
## 0.5939810 0.5181391
## as.factor(EDUC_CAT)College+ as.factor(MARRY)3
## 0.4261731 1.9818516
## as.factor(MARRY)4 as.factor(MARRY)5
## 1.0451982 1.0481816
## as.factor(MARRY)6 as.factor(MARRY)8
## 1.1709608 2.1308046
## BMI AVGSMK
## 0.9868769 1.0049872
## SIZE
## 0.9766748
exp(confint(poisson))
## Waiting for profiling to be done...
## 2.5 % 97.5 %
## (Intercept) 0.0931873 0.1734571
## as.factor(BOOZE_q)0–0.5/week 0.7513986 1.0120424
## as.factor(BOOZE_q)0.5–2/week 0.5970498 0.7720081
## as.factor(BOOZE_q)>2/week 0.6408378 0.8056426
## SEX 0.4275597 0.5179443
## as.factor(RACE)2 0.6727843 0.9006519
## as.factor(RACE)3 0.3706034 0.8219676
## as.factor(EDUC_CAT)Some HS 0.6999102 0.8919260
## as.factor(EDUC_CAT)HS Grad 0.5314196 0.6635397
## as.factor(EDUC_CAT)Some College 0.4396896 0.6076562
## as.factor(EDUC_CAT)College+ 0.3570011 0.5058604
## as.factor(MARRY)3 1.7535386 2.2354530
## as.factor(MARRY)4 0.8506068 1.2701349
## as.factor(MARRY)5 0.7802367 1.3764588
## as.factor(MARRY)6 0.9646147 1.4074806
## as.factor(MARRY)8 1.0195924 3.8653838
## BMI 0.9778251 0.9958847
## AVGSMK 1.0017430 1.0081514
## SIZE 0.9605928 0.9930919
#Table 2
# Age-adjusted Cox model
cox_age <- coxph(Surv(FU, DEATH) ~ as.factor(BOOZE_q) + AGEYRS, data = d)
# Age-adjusted Poisson model
poisson_age <- glm(DEATH ~ as.factor(BOOZE_q) + AGEYRS,
family = poisson(link = "log"),
offset = log(FU), data = d_pois)
# View results
summary(cox_age)
## Call:
## coxph(formula = Surv(FU, DEATH) ~ as.factor(BOOZE_q) + AGEYRS,
## data = d)
##
## n= 9250, number of events= 2145
##
## coef exp(coef) se(coef) z Pr(>|z|)
## as.factor(BOOZE_q)0–0.5/week 0.014101 1.014201 0.075143 0.188 0.8511
## as.factor(BOOZE_q)0.5–2/week -0.037072 0.963606 0.063688 -0.582 0.5605
## as.factor(BOOZE_q)>2/week 0.130416 1.139303 0.053551 2.435 0.0149 *
## AGEYRS 0.088717 1.092771 0.002572 34.492 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## as.factor(BOOZE_q)0–0.5/week 1.0142 0.9860 0.8753 1.175
## as.factor(BOOZE_q)0.5–2/week 0.9636 1.0378 0.8505 1.092
## as.factor(BOOZE_q)>2/week 1.1393 0.8777 1.0258 1.265
## AGEYRS 1.0928 0.9151 1.0873 1.098
##
## Concordance= 0.751 (se = 0.005 )
## Likelihood ratio test= 1789 on 4 df, p=<2e-16
## Wald test = 1211 on 4 df, p=<2e-16
## Score (logrank) test = 1496 on 4 df, p=<2e-16
summary(poisson_age)
##
## Call:
## glm(formula = DEATH ~ as.factor(BOOZE_q) + AGEYRS, family = poisson(link = "log"),
## data = d_pois, offset = log(FU))
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -9.192240 0.169248 -54.312 <2e-16 ***
## as.factor(BOOZE_q)0–0.5/week 0.008057 0.075291 0.107 0.9148
## as.factor(BOOZE_q)0.5–2/week -0.035776 0.063783 -0.561 0.5749
## as.factor(BOOZE_q)>2/week 0.125441 0.053535 2.343 0.0191 *
## AGEYRS 0.086236 0.002549 33.837 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for poisson family taken to be 1)
##
## Null deviance: 9335.0 on 9245 degrees of freedom
## Residual deviance: 7625.5 on 9241 degrees of freedom
## AIC: 11918
##
## Number of Fisher Scoring iterations: 6
# Tidy model outputs
cox_age_tidy <- tidy(cox_age, exponentiate = TRUE, conf.int = TRUE)
cox_full_tidy <- tidy(cox, exponentiate = TRUE, conf.int = TRUE)
poisson_age_tidy <- tidy(poisson_age, exponentiate = TRUE, conf.int = TRUE)
poisson_full_tidy<- tidy(poisson, exponentiate = TRUE, conf.int = TRUE)
booze_terms <- function(df, model_name) {
df %>%
filter(grepl("BOOZE_q", term)) %>%
mutate(model = model_name,
estimate_CI = paste0(round(estimate, 2), " (",
round(conf.low, 2), ", ",
round(conf.high, 2), ")")) %>%
select(term, model, estimate_CI)
}
results <- bind_rows(
booze_terms(cox_age_tidy, "Cox Age-adjusted"),
booze_terms(cox_full_tidy, "Cox Full-adjusted"),
booze_terms(poisson_age_tidy, "Poisson Age-adjusted"),
booze_terms(poisson_full_tidy, "Poisson Full-adjusted")
)
table2_clean <- results %>%
pivot_wider(names_from = model, values_from = estimate_CI)
#Table 3
tidy(cox_men, exponentiate = TRUE, conf.int = TRUE)
## # A tibble: 20 × 7
## term estimate std.error statistic p.value conf.low conf.high
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 as.factor(BOOZE_q)… 1.03 0.106 0.292 7.71e- 1 0.838 1.27
## 2 as.factor(BOOZE_q)… 0.856 0.0840 -1.84 6.51e- 2 0.726 1.01
## 3 as.factor(BOOZE_q)… 0.885 0.0702 -1.74 8.23e- 2 0.771 1.02
## 4 AGEYRS 1.10 0.00359 26.3 4.31e-152 1.09 1.11
## 5 as.factor(RACE)2 0.883 0.0991 -1.26 2.09e- 1 0.727 1.07
## 6 as.factor(RACE)3 0.578 0.263 -2.08 3.72e- 2 0.346 0.968
## 7 as.factor(EDUC_CAT… 1.04 0.0803 0.523 6.01e- 1 0.891 1.22
## 8 as.factor(EDUC_CAT… 0.935 0.0767 -0.872 3.83e- 1 0.805 1.09
## 9 as.factor(EDUC_CAT… 0.833 0.108 -1.70 8.91e- 2 0.674 1.03
## 10 as.factor(EDUC_CAT… 0.648 0.116 -3.74 1.88e- 4 0.517 0.814
## 11 as.factor(MARRY)3 1.14 0.113 1.15 2.51e- 1 0.913 1.42
## 12 as.factor(MARRY)4 1.48 0.131 2.98 2.85e- 3 1.14 1.91
## 13 as.factor(MARRY)5 1.39 0.184 1.79 7.30e- 2 0.970 2.00
## 14 as.factor(MARRY)6 1.41 0.126 2.72 6.58e- 3 1.10 1.80
## 15 as.factor(MARRY)8 1.62 0.453 1.07 2.85e- 1 0.668 3.94
## 16 BMI 0.982 0.00729 -2.54 1.09e- 2 0.968 0.996
## 17 AVGSMK 1.02 0.00187 10.4 2.20e- 25 1.02 1.02
## 18 as.factor(SIZE_CAT… 1.05 0.0929 0.534 5.93e- 1 0.876 1.26
## 19 as.factor(SIZE_CAT… 1.12 0.0966 1.13 2.58e- 1 0.923 1.35
## 20 as.factor(SIZE_CAT… 0.968 0.0704 -0.458 6.47e- 1 0.843 1.11
tidy(cox_women, exponentiate = TRUE, conf.int = TRUE)
## # A tibble: 20 × 7
## term estimate std.error statistic p.value conf.low conf.high
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 as.factor(BOOZE_q)… 0.967 0.109 -0.307 7.59e- 1 0.781 1.20
## 2 as.factor(BOOZE_q)… 0.841 0.105 -1.65 9.84e- 2 0.684 1.03
## 3 as.factor(BOOZE_q)… 0.949 0.107 -0.487 6.26e- 1 0.770 1.17
## 4 AGEYRS 1.10 0.00446 21.3 8.20e-101 1.09 1.11
## 5 as.factor(RACE)2 1.06 0.118 0.493 6.22e- 1 0.841 1.34
## 6 as.factor(RACE)3 0.987 0.307 -0.0423 9.66e- 1 0.541 1.80
## 7 as.factor(EDUC_CAT… 0.960 0.0987 -0.414 6.79e- 1 0.791 1.16
## 8 as.factor(EDUC_CAT… 0.968 0.0886 -0.367 7.13e- 1 0.814 1.15
## 9 as.factor(EDUC_CAT… 0.830 0.131 -1.42 1.55e- 1 0.642 1.07
## 10 as.factor(EDUC_CAT… 0.932 0.139 -0.511 6.10e- 1 0.710 1.22
## 11 as.factor(MARRY)3 1.07 0.0780 0.812 4.17e- 1 0.914 1.24
## 12 as.factor(MARRY)4 0.858 0.163 -0.942 3.46e- 1 0.624 1.18
## 13 as.factor(MARRY)5 1.26 0.238 0.983 3.26e- 1 0.792 2.02
## 14 as.factor(MARRY)6 0.980 0.156 -0.130 8.96e- 1 0.722 1.33
## 15 as.factor(MARRY)8 0.994 0.505 -0.0114 9.91e- 1 0.369 2.68
## 16 BMI 1.00 0.00642 -0.0406 9.68e- 1 0.987 1.01
## 17 AVGSMK 1.03 0.00267 9.88 4.87e- 23 1.02 1.03
## 18 as.factor(SIZE_CAT… 1.03 0.105 0.267 7.90e- 1 0.837 1.26
## 19 as.factor(SIZE_CAT… 0.910 0.110 -0.859 3.90e- 1 0.734 1.13
## 20 as.factor(SIZE_CAT… 0.986 0.0850 -0.168 8.67e- 1 0.835 1.16
tidy(cox_product)
## # A tibble: 24 × 5
## term estimate std.error statistic p.value
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 as.factor(BOOZE_q)0–0.5/week 0.0786 0.236 0.332 7.40e- 1
## 2 as.factor(BOOZE_q)0.5–2/week -0.181 0.194 -0.934 3.50e- 1
## 3 as.factor(BOOZE_q)>2/week -0.236 0.167 -1.42 1.56e- 1
## 4 SEX -0.652 0.0648 -10.1 8.98e- 24
## 5 AGEYRS 0.0947 0.00278 34.1 6.05e-255
## 6 as.factor(RACE)2 -0.0432 0.0754 -0.573 5.67e- 1
## 7 as.factor(RACE)3 -0.344 0.199 -1.73 8.42e- 2
## 8 as.factor(EDUC_CAT)Some HS 0.00447 0.0622 0.0718 9.43e- 1
## 9 as.factor(EDUC_CAT)HS Grad -0.0619 0.0577 -1.07 2.84e- 1
## 10 as.factor(EDUC_CAT)Some College -0.206 0.0829 -2.48 1.30e- 2
## # ℹ 14 more rows